2023
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Masters Theses
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Xander Beurden Stability control and positional water jet placement for a novel tethered unmanned hydro-propelled aerial vehicle using real-time water jet detection (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Wagter, C. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:a4f3199c-71f6-4182-bd98-30db62db8018,
title = {Stability control and positional water jet placement for a novel tethered unmanned hydro-propelled aerial vehicle using real-time water jet detection},
author = {Xander Beurden},
url = {http://resolver.tudelft.nl/uuid:a4f3199c-71f6-4182-bd98-30db62db8018},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Aerial platforms designed for water jet placement are gaining interest in the areas of fire-fighting, washing, and irrigation. A novel, lightweight, and simplistic design is proposed that reduces the number of actuators and limits ineffective water discharge. External camera feedback was used for position control as a first step towards autonomous flight. An initial prototype of an unmanned hydro-propelled aerial vehicle (UHAV) connected to a water hose was designed and fabricated. Flight tests were conducted to show that attitude control with uniaxial thrust-vectoring of two nozzles was impossible due to undamped vibrations and coupling effects. By redesigning the PID controller, pitch rate damping was accomplished. Furthermore, a design trade-off led to the introduction of a canting keel to reduce bank-yaw coupling effects due to asymmetric nozzle deflections. Flight tests proved that the iterated design with a hose length of 3m was capable of disturbance rejection and setpoint tracking. An external camera was used to show that the Lucas-Kanade optical flow algorithm and the implementation of the YOLOv5 segmentation model can be used for positional water jet placement. By increasing the pitch rate damping, improving the water jet detection algorithm and implementing a cost function for water discharge at the area of interest, autonomous missions can be flown in the future.},
note = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Aerial platforms designed for water jet placement are gaining interest in the areas of fire-fighting, washing, and irrigation. A novel, lightweight, and simplistic design is proposed that reduces the number of actuators and limits ineffective water discharge. External camera feedback was used for position control as a first step towards autonomous flight. An initial prototype of an unmanned hydro-propelled aerial vehicle (UHAV) connected to a water hose was designed and fabricated. Flight tests were conducted to show that attitude control with uniaxial thrust-vectoring of two nozzles was impossible due to undamped vibrations and coupling effects. By redesigning the PID controller, pitch rate damping was accomplished. Furthermore, a design trade-off led to the introduction of a canting keel to reduce bank-yaw coupling effects due to asymmetric nozzle deflections. Flight tests proved that the iterated design with a hose length of 3m was capable of disturbance rejection and setpoint tracking. An external camera was used to show that the Lucas-Kanade optical flow algorithm and the implementation of the YOLOv5 segmentation model can be used for positional water jet placement. By increasing the pitch rate damping, improving the water jet detection algorithm and implementing a cost function for water discharge at the area of interest, autonomous missions can be flown in the future. |
Tim Burgers Evolving Spiking Neural Networks to Mimic PID Control: Applied to Autonomous Blimps (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Stroobants, S. (mentor); de Wagter, C. (graduation committee); Bombelli, A. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:1edec476-3b58-458d-a4a6-cbba30b783e6,
title = {Evolving Spiking Neural Networks to Mimic PID Control: Applied to Autonomous Blimps},
author = {Tim Burgers},
url = {http://resolver.tudelft.nl/uuid:1edec476-3b58-458d-a4a6-cbba30b783e6},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps, known for their extended endurance, power-efficient control methods are essential. Spiking neural networks (SNN) can provide a solution, facilitating energy-efficient and asynchronous event-driven processing. <br/>In this paper, we have evolved SNNs for accurate altitude control of a non-neutrally buoyant indoor blimp, relying solely on onboard sensing and processing power. The blimp's altitude tracking performance significantly improved compared to prior research, showing reduced oscillations and a minimal steady-state error. The parameters of the SNNs were optimized via an evolutionary algorithm, using a Proportional-Derivative-Integral (PID) controller as the target signal. We developed two complementary SNN controllers while examining various hidden layer structures. The first controller responds swiftly to control errors, mitigating overshooting and oscillations, while the second minimizes steady-state errors due to non-neutral buoyancy-induced drift. Despite the blimp's drivetrain limitations, our SNN controllers ensured stable altitude control, employing only 160 spiking neurons.},
note = {de Croon, G.C.H.E. (mentor); Stroobants, S. (mentor); de Wagter, C. (graduation committee); Bombelli, A. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps, known for their extended endurance, power-efficient control methods are essential. Spiking neural networks (SNN) can provide a solution, facilitating energy-efficient and asynchronous event-driven processing. <br/>In this paper, we have evolved SNNs for accurate altitude control of a non-neutrally buoyant indoor blimp, relying solely on onboard sensing and processing power. The blimp's altitude tracking performance significantly improved compared to prior research, showing reduced oscillations and a minimal steady-state error. The parameters of the SNNs were optimized via an evolutionary algorithm, using a Proportional-Derivative-Integral (PID) controller as the target signal. We developed two complementary SNN controllers while examining various hidden layer structures. The first controller responds swiftly to control errors, mitigating overshooting and oscillations, while the second minimizes steady-state errors due to non-neutral buoyancy-induced drift. Despite the blimp's drivetrain limitations, our SNN controllers ensured stable altitude control, employing only 160 spiking neurons. |
Seamus McGinley Vision-guided Quadrotor Perching on Imperfectly Cylindrical Structures (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Hamaza, S. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:21b4203a-abbb-47d7-bbd5-df042d8d7b53,
title = {Vision-guided Quadrotor Perching on Imperfectly Cylindrical Structures},
author = {Seamus McGinley},
url = {http://resolver.tudelft.nl/uuid:21b4203a-abbb-47d7-bbd5-df042d8d7b53},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {The design of aerial robots capable of perching poses significant challenges, from requiring pilots to master precise manoeuvres, to devising hardware and software capable of adapting to diverse perch structures and complex field environments. The Slapper drone presented in this paper tackles these challenges through three main innovations. First, a lightweight, vision-based system for autonomous perch detection using onboard flight hardware detects (imperfect) cylindrical objects found in both natural and artificial environments. Second, an onboard flight planning algorithm autonomously handles the detection, approach and perching flight phases, removing the need for a pilot. Third, a completely passive gripper utilises bistable shell structures to allow for perching on general long narrow features without any precise control inputs or power consumption. This design was successfully validated through both simulation and multiple indoor flights to result in reliable autonomous quadrotor perching in real-world environments.},
note = {Hamaza, S. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The design of aerial robots capable of perching poses significant challenges, from requiring pilots to master precise manoeuvres, to devising hardware and software capable of adapting to diverse perch structures and complex field environments. The Slapper drone presented in this paper tackles these challenges through three main innovations. First, a lightweight, vision-based system for autonomous perch detection using onboard flight hardware detects (imperfect) cylindrical objects found in both natural and artificial environments. Second, an onboard flight planning algorithm autonomously handles the detection, approach and perching flight phases, removing the need for a pilot. Third, a completely passive gripper utilises bistable shell structures to allow for perching on general long narrow features without any precise control inputs or power consumption. This design was successfully validated through both simulation and multiple indoor flights to result in reliable autonomous quadrotor perching in real-world environments. |
Michiel Firlefyn Direct Learning of Home Vector Direction: Incited by Existing Insect-Inspired Approaches for Local Navigation and Wayfinding (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:98b694a1-72fc-4a4f-907b-d511cc7f9bdd,
title = {Direct Learning of Home Vector Direction: Incited by Existing Insect-Inspired Approaches for Local Navigation and Wayfinding},
author = {Michiel Firlefyn},
url = {http://resolver.tudelft.nl/uuid:98b694a1-72fc-4a4f-907b-d511cc7f9bdd},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Insects have long been recognized for their ability to navigate and return home using visual cues from their nest’s environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learning flights of honey bees and wasps, we propose a robot navigation method based on directly learning the home vector directions from visual percepts during the learning flight. Subsequently, the robot will travel away from the nest, come back by odometric means, and eliminate the resultant drift by inferring the home vector orientation from the currently experienced view. In this study, a convolutional neural network is employed as learning mechanism in both simulated and real forest environments. Additionally, a comprehensive performance analysis reveals that the network’s homing abilities closely resemble those observed in real insects, all while only utilizing visual and odometric senses. If all images contain sufficient texture and illumination, the average errors<br/>of the inferred home vectors remain below 24°. Moreover, our investigation reveals a noteworthy insight: the trajectory followed during the initial learning flight, for sample image acquisition, exerts a pronounced impact on the network’s output. For instance, a higher density of sample points in proximity to the nest results in a more consistent return.},
note = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Insects have long been recognized for their ability to navigate and return home using visual cues from their nest’s environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learning flights of honey bees and wasps, we propose a robot navigation method based on directly learning the home vector directions from visual percepts during the learning flight. Subsequently, the robot will travel away from the nest, come back by odometric means, and eliminate the resultant drift by inferring the home vector orientation from the currently experienced view. In this study, a convolutional neural network is employed as learning mechanism in both simulated and real forest environments. Additionally, a comprehensive performance analysis reveals that the network’s homing abilities closely resemble those observed in real insects, all while only utilizing visual and odometric senses. If all images contain sufficient texture and illumination, the average errors<br/>of the inferred home vectors remain below 24°. Moreover, our investigation reveals a noteworthy insight: the trajectory followed during the initial learning flight, for sample image acquisition, exerts a pronounced impact on the network’s output. For instance, a higher density of sample points in proximity to the nest results in a more consistent return. |
MOJI SHI Evaluating Dynamic Environment Difficulty for Obstacle Avoidance Benchmarking (Masters Thesis) TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics, 2023, (Alonso Mora, J. (mentor); Chen, G. (mentor); Wisse, M. (graduation committee); Hamaza, S. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:ca95c8cb-8df3-4d43-9d17-c7b7f54eb1ea,
title = {Evaluating Dynamic Environment Difficulty for Obstacle Avoidance Benchmarking},
author = {MOJI SHI},
url = {http://resolver.tudelft.nl/uuid:ca95c8cb-8df3-4d43-9d17-c7b7f54eb1ea},
year = {2023},
date = {2023-01-01},
school = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics},
abstract = {Dynamic obstacle avoidance remains a crucial research area for autonomous systems, such as Micro Aerial Vehicles (MAVs) and service robots. <br/>Efforts to develop dynamic collision avoidance techniques in unknown environments have proliferated in recent years. While these methods exhibit impressive and reliable performance in simpler environments, their efficacy in more challenging settings remains an area ripe for enhancement. The difficulty of these environments arises from a multitude of factors, and currently, no standardized approach exists to quantify this complexity. Additionally, to fairly compare different dynamic collision avoidance strategies, it's essential to assess them in environments with a similar degree of difficulty. Therefore, devising a metric capable of accurately gauging the intricacy of dynamic environments becomes imperative.<br/><br/><br/>Building on this context, this master's thesis endeavors to fill this critical gap through three contributions: 1) The establishment and validation of map difficulty metrics that represent the difficulty of dynamic environments, 2) The introduction of a robust benchmarking pipeline to critically validate the representativeness of the proposed metrics and evaluate various collision avoidance strategies, and 3) The provision of a framework for comparative analysis of different planning strategies, utilizing the introduced map difficulty metric. <br/><br/>The proposed survivability metric effectively captures environmental complexity. Its validity is evidenced by a notable correlation with the success rates of typical collision avoidance methods, with over 1.7 million collision avoidance trials on over six hundred maps, securing a Spearman's Rank correlation coefficient (SRCC) of over 0.9. This metric serves as an indispensable tool for facilitating fair comparisons in this dynamic research domain. More importantly, it offers valuable insights for the future refinement and improvement of dynamic collision avoidance strategies, making a contribution to the continuous advancement of autonomous systems.},
note = {Alonso Mora, J. (mentor); Chen, G. (mentor); Wisse, M. (graduation committee); Hamaza, S. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Dynamic obstacle avoidance remains a crucial research area for autonomous systems, such as Micro Aerial Vehicles (MAVs) and service robots. <br/>Efforts to develop dynamic collision avoidance techniques in unknown environments have proliferated in recent years. While these methods exhibit impressive and reliable performance in simpler environments, their efficacy in more challenging settings remains an area ripe for enhancement. The difficulty of these environments arises from a multitude of factors, and currently, no standardized approach exists to quantify this complexity. Additionally, to fairly compare different dynamic collision avoidance strategies, it's essential to assess them in environments with a similar degree of difficulty. Therefore, devising a metric capable of accurately gauging the intricacy of dynamic environments becomes imperative.<br/><br/><br/>Building on this context, this master's thesis endeavors to fill this critical gap through three contributions: 1) The establishment and validation of map difficulty metrics that represent the difficulty of dynamic environments, 2) The introduction of a robust benchmarking pipeline to critically validate the representativeness of the proposed metrics and evaluate various collision avoidance strategies, and 3) The provision of a framework for comparative analysis of different planning strategies, utilizing the introduced map difficulty metric. <br/><br/>The proposed survivability metric effectively captures environmental complexity. Its validity is evidenced by a notable correlation with the success rates of typical collision avoidance methods, with over 1.7 million collision avoidance trials on over six hundred maps, securing a Spearman's Rank correlation coefficient (SRCC) of over 0.9. This metric serves as an indispensable tool for facilitating fair comparisons in this dynamic research domain. More importantly, it offers valuable insights for the future refinement and improvement of dynamic collision avoidance strategies, making a contribution to the continuous advancement of autonomous systems. |
Youssef Farah EV-LayerSegNet: Self-supervised Motion Segmentation using Event-based Cameras (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Mooij, E. (graduation committee); Ellerbroek, Joost (graduation committee); Paredes Valles, F. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:bcac496c-6757-4067-b1dd-5d8356486bf8,
title = {EV-LayerSegNet: Self-supervised Motion Segmentation using Event-based Cameras},
author = {Youssef Farah},
url = {http://resolver.tudelft.nl/uuid:bcac496c-6757-4067-b1dd-5d8356486bf8},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Event cameras are novel bio-inspired sensors that capture motion dynamics with much higher temporal resolution than traditional cameras, since pixels react asynchronously to brightness changes. They are therefore better suited for tasks involving motion such as motion segmentation. However, training event-based networks still represents a difficult challenge, as obtaining ground truth is very expensive and error-prone. In this article, we introduce EV-LayerSegNet, the first self-supervised CNN for event-based motion segmentation. Inspired by a layered representation of the scene dynamics, we show that it is possible to learn affine optical flow and segmentation masks separately, and use them to deblur the input events. The deblurring quality is then measured and used as self-supervised learning loss.},
note = {de Croon, G.C.H.E. (mentor); Mooij, E. (graduation committee); Ellerbroek, Joost (graduation committee); Paredes Valles, F. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Event cameras are novel bio-inspired sensors that capture motion dynamics with much higher temporal resolution than traditional cameras, since pixels react asynchronously to brightness changes. They are therefore better suited for tasks involving motion such as motion segmentation. However, training event-based networks still represents a difficult challenge, as obtaining ground truth is very expensive and error-prone. In this article, we introduce EV-LayerSegNet, the first self-supervised CNN for event-based motion segmentation. Inspired by a layered representation of the scene dynamics, we show that it is possible to learn affine optical flow and segmentation masks separately, and use them to deblur the input events. The deblurring quality is then measured and used as self-supervised learning loss. |
Laurens Lammers Memory Mechanisms in Spiking Neural Networks (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Hagenaars, J.J. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:e52f0ee0-a859-4177-80e4-268dfd65deca,
title = {Memory Mechanisms in Spiking Neural Networks},
author = {Laurens Lammers},
url = {http://resolver.tudelft.nl/uuid:e52f0ee0-a859-4177-80e4-268dfd65deca},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Neuromorphic sensors, like for example event cameras, detect incremental changes in the sensed quantity and communicate these via a stream of events. Desired properties of these signals such as high temporal resolution and asynchrony are not always fully exploited by algorithms that process these signals. Spiking neural networks (SNNs) have emerged as the algorithms that promise to maximally attain these characteristics and are likely the key to achieving a fully neuromorphic computing pipeline. But, this means that if the SNN is to take full advantage, the event stream must be sent directly and unaltered to the SNN, which in turn implies that all temporal integration should occur inside the SNN. Therefore, it is interesting to investigate the mechanisms that achieve this. This thesis does so through evaluating and comparing the performance of different memory mechanisms in SNNs found in the literature, as well as through an in depth analysis of the inner workings of these mechanisms. The mechanisms include spiking neural dynamics (leaks and thresholds), explicit recurrent connections, and propagation delays. We demonstrate our concepts on two small scale generated 1D moving pixel tasks in preliminary experiments first. After that, we extend our research to compare the memory mechanisms on a real-world neuromorphic vision processing task, in which the networks regress angular velocity given event based input. We find that both explicit recurrency and delays improve the prediction accuracy of the SNN, compared to having just spiking neuronal dynamics. Analysis of the inner workings of the networks shows that the threshold and reset mechanism of spiking neurons play an important role in allowing longer neuron timescales (lower membrane leak). Forgetting (at the right time) turns out to play an important role in memory. Additionally, it becomes apparent that optimizing an SNN with explicit recurrent connections or learnable delays does not lead to the formation of robust spiking neuronal dynamics. In fact, spiking neuronal dynamics are largely ignored, as after optimization virtually no input current is integrated onto the membrane potential in these cases. Instead, we consistently find that a recurrent SNN prefers to build a state solely with the explicit recurrent connections, while an SNN with delays prefers to just use the delays. Therefore, our SNNs with explicit recurrent connections and delays are in fact better described as binary activated RNNs and ANNs, respectively.},
note = {Hagenaars, J.J. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Neuromorphic sensors, like for example event cameras, detect incremental changes in the sensed quantity and communicate these via a stream of events. Desired properties of these signals such as high temporal resolution and asynchrony are not always fully exploited by algorithms that process these signals. Spiking neural networks (SNNs) have emerged as the algorithms that promise to maximally attain these characteristics and are likely the key to achieving a fully neuromorphic computing pipeline. But, this means that if the SNN is to take full advantage, the event stream must be sent directly and unaltered to the SNN, which in turn implies that all temporal integration should occur inside the SNN. Therefore, it is interesting to investigate the mechanisms that achieve this. This thesis does so through evaluating and comparing the performance of different memory mechanisms in SNNs found in the literature, as well as through an in depth analysis of the inner workings of these mechanisms. The mechanisms include spiking neural dynamics (leaks and thresholds), explicit recurrent connections, and propagation delays. We demonstrate our concepts on two small scale generated 1D moving pixel tasks in preliminary experiments first. After that, we extend our research to compare the memory mechanisms on a real-world neuromorphic vision processing task, in which the networks regress angular velocity given event based input. We find that both explicit recurrency and delays improve the prediction accuracy of the SNN, compared to having just spiking neuronal dynamics. Analysis of the inner workings of the networks shows that the threshold and reset mechanism of spiking neurons play an important role in allowing longer neuron timescales (lower membrane leak). Forgetting (at the right time) turns out to play an important role in memory. Additionally, it becomes apparent that optimizing an SNN with explicit recurrent connections or learnable delays does not lead to the formation of robust spiking neuronal dynamics. In fact, spiking neuronal dynamics are largely ignored, as after optimization virtually no input current is integrated onto the membrane potential in these cases. Instead, we consistently find that a recurrent SNN prefers to build a state solely with the explicit recurrent connections, while an SNN with delays prefers to just use the delays. Therefore, our SNNs with explicit recurrent connections and delays are in fact better described as binary activated RNNs and ANNs, respectively. |
Mauro Villanueva Aguado An Adaptive Neural Network Quadrotor Trajectory Tracking Controller Tolerant to Propeller Damage (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Wagter, C. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:232b5015-df70-424b-91ab-149ed4d8416a,
title = {An Adaptive Neural Network Quadrotor Trajectory Tracking Controller Tolerant to Propeller Damage},
author = {Mauro Villanueva Aguado},
url = {http://resolver.tudelft.nl/uuid:232b5015-df70-424b-91ab-149ed4d8416a},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {class="MsoNormal" style="margin-bottom:0cm;line-height:normal">Executing quadrotor trajectories accurately and therefore safely is a challenging task. State-of-the-art adaptive controllers achieve impressive trajectory tracking results with slight performance degradation in varying winds or payloads, but at the cost of computational complexity. Requiring additional embedded computers onboard, adding weight and requiring power. Given the limited computational resources onboard, a trade-off between accuracy and complexity must be considered. To this end, we implement "Neural-Fly" a lightweight adaptive neural controller to adapt to propeller damage, a common occurrence in real-world flight. The adaptive neural architecture consists of two components: (I) offline learning of a condition invariant representation of the aerodynamic forces through Deep Neural Networks (II) fast online adaptation to the current propeller condition using a composite adaptation law. We deploy this flight controller fully onboard the flight controller of the Parrot Bebop 1,showcasing its computational efficiency. The adaptive neural controller improves tracking performance by ≈60% over the nonlinear baseline, with minimal performance degradation of just ≈20% with increasing propeller damage.},
note = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
class="MsoNormal" style="margin-bottom:0cm;line-height:normal">Executing quadrotor trajectories accurately and therefore safely is a challenging task. State-of-the-art adaptive controllers achieve impressive trajectory tracking results with slight performance degradation in varying winds or payloads, but at the cost of computational complexity. Requiring additional embedded computers onboard, adding weight and requiring power. Given the limited computational resources onboard, a trade-off between accuracy and complexity must be considered. To this end, we implement "Neural-Fly" a lightweight adaptive neural controller to adapt to propeller damage, a common occurrence in real-world flight. The adaptive neural architecture consists of two components: (I) offline learning of a condition invariant representation of the aerodynamic forces through Deep Neural Networks (II) fast online adaptation to the current propeller condition using a composite adaptation law. We deploy this flight controller fully onboard the flight controller of the Parrot Bebop 1,showcasing its computational efficiency. The adaptive neural controller improves tracking performance by ≈60% over the nonlinear baseline, with minimal performance degradation of just ≈20% with increasing propeller damage. |
Jonathas Laffita van den Hove d'Ertsenryck Rigid airborne docking between a fixed-wing UAV and an over-actuated multicopter (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:6b397485-750f-4e97-99ec-536ae2933d60,
title = {Rigid airborne docking between a fixed-wing UAV and an over-actuated multicopter},
author = {Jonathas Laffita van den Hove d'Ertsenryck},
url = {http://resolver.tudelft.nl/uuid:6b397485-750f-4e97-99ec-536ae2933d60},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Fixed-wing aircraft fly longer, faster, and further than rotorcraft, but cannot take off or land vertically. Hybrid drones combine VTOL with a wing for forward flight, but the hovering system generally makes them less efficient than a pure fixed-wing. We propose an alternative, in which a rotorcraft is used to assist the fixed-wing UAV with the VTOL portions of the flight. This paper takes the first steps towards this alternative by developing and testing an overactuated rotorcraft that can autonomously dock onto a target at fixed-wing velocities. The control system uses Incremental Non-Linear Dynamic Inversion Control (INDI) to achieve linear accelerations with lateral and longitudinal motors, enabling robust horizontal control independent of attitude. A relative guidance algorithm for the docking approach path is presented, along with a vision sensing approach using ArUco markers and IR LEDs. Successful docking and separation were achieved in the wind tunnel at speeds of up to $15$m/s.},
note = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Fixed-wing aircraft fly longer, faster, and further than rotorcraft, but cannot take off or land vertically. Hybrid drones combine VTOL with a wing for forward flight, but the hovering system generally makes them less efficient than a pure fixed-wing. We propose an alternative, in which a rotorcraft is used to assist the fixed-wing UAV with the VTOL portions of the flight. This paper takes the first steps towards this alternative by developing and testing an overactuated rotorcraft that can autonomously dock onto a target at fixed-wing velocities. The control system uses Incremental Non-Linear Dynamic Inversion Control (INDI) to achieve linear accelerations with lateral and longitudinal motors, enabling robust horizontal control independent of attitude. A relative guidance algorithm for the docking approach path is presented, along with a vision sensing approach using ArUco markers and IR LEDs. Successful docking and separation were achieved in the wind tunnel at speeds of up to $15$m/s. |
Erwin Lodder Neuro-evolution learned neuromorphic control for a vision-based 3D landing (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Stroobants, S. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:5135b7b8-3c4c-46cf-a0cc-e2fbf6da5fff,
title = {Neuro-evolution learned neuromorphic control for a vision-based 3D landing},
author = {Erwin Lodder},
url = {http://resolver.tudelft.nl/uuid:5135b7b8-3c4c-46cf-a0cc-e2fbf6da5fff},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
note = {de Croon, G.C.H.E. (mentor); Stroobants, S. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
|
Frédéric Larocque Synthetic Air Data System for Pitot Tube Failure Detection on the Variable Skew Quad Plane (Masters Thesis) TU Delft Aerospace Engineering; TU Delft Control & Simulation, 2023, (Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); De Ponti, T.M.L. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:5d786e19-6871-4478-bda8-43f7cab20633,
title = {Synthetic Air Data System for Pitot Tube Failure Detection on the Variable Skew Quad Plane},
author = {Frédéric Larocque},
url = {http://resolver.tudelft.nl/uuid:5d786e19-6871-4478-bda8-43f7cab20633},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
abstract = {Pitot tube-free airspeed estimation methods exist for fixed-wing and multirotor configurations, but lack direct applicability to hybrid unmanned air vehicles due to their wide flight envelope and changing dynamics during transition. This work proposes a novel synthetic air data system for the Variable Skew Quad Plane (VSQP) hybrid vehicle to allow airspeed estimation from hover to high speed forward flight and provide pitot tube fault detection. An Extended Kalman Filter fuses Global Navigation Satellite System (GNSS) and inertial measurements using model-independent kinematics equations to estimate wind and airspeed without the use of the pitot tube. The filter is augmented by a simplified vehicle force model. Pitot tube fault detection is achieved with a simple thresholding operation on the pitot tube measurement and the airspeed estimation residual. Accurate airspeed estimation was validated with logged test flight data, achieving an overall 1.62 m/s root mean square error. Using the airspeed estimation, quick detection (0.16 s) of a real-life abrupt pitot tube fault was demonstrated. This new airspeed estimation method provides an innovative approach for increasing the fault tolerance of the VSQP and similar quad-plane vehicles.},
note = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); De Ponti, T.M.L. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Pitot tube-free airspeed estimation methods exist for fixed-wing and multirotor configurations, but lack direct applicability to hybrid unmanned air vehicles due to their wide flight envelope and changing dynamics during transition. This work proposes a novel synthetic air data system for the Variable Skew Quad Plane (VSQP) hybrid vehicle to allow airspeed estimation from hover to high speed forward flight and provide pitot tube fault detection. An Extended Kalman Filter fuses Global Navigation Satellite System (GNSS) and inertial measurements using model-independent kinematics equations to estimate wind and airspeed without the use of the pitot tube. The filter is augmented by a simplified vehicle force model. Pitot tube fault detection is achieved with a simple thresholding operation on the pitot tube measurement and the airspeed estimation residual. Accurate airspeed estimation was validated with logged test flight data, achieving an overall 1.62 m/s root mean square error. Using the airspeed estimation, quick detection (0.16 s) of a real-life abrupt pitot tube fault was demonstrated. This new airspeed estimation method provides an innovative approach for increasing the fault tolerance of the VSQP and similar quad-plane vehicles. |
Marvin Cairo Energy poverty, bridging the gap between housing association and tenant: What measures housing associations can take to aid their tenants who are struggling with energy poverty (Masters Thesis) TU Delft Architecture and the Built Environment, 2023, (Hoekstra, J.S.C.M. (mentor); Qian, QK (mentor); Croon, T.M. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:3fcc6230-86fc-4848-a6ed-16dd46fea640,
title = {Energy poverty, bridging the gap between housing association and tenant: What measures housing associations can take to aid their tenants who are struggling with energy poverty},
author = {Marvin Cairo},
url = {http://resolver.tudelft.nl/uuid:3fcc6230-86fc-4848-a6ed-16dd46fea640},
year = {2023},
date = {2023-01-01},
school = {TU Delft Architecture and the Built Environment},
abstract = {Due to rising energy prices, an increasing number of households are experiencing difficulties with the affordability of their energy bills. As a result, households are unable to heat or cool their homes, or use electrical appliances as desired. This is known as energy poverty. This research focuses on energy poverty within housing associations. As two-thirds of households experiencing energy poverty live in housing association homes, this research is specifically targeted at housing associations. The research examines the possible gap between what housing associations are doing to combat energy poverty for their tenants, and what tenants would like to see housing associations do for them. Since renovation is simply too expensive and takes several years, it is excluded from consideration. As a result, housing associations will need to take other measures to help their tenants. This research will look at these taken measures and provides recommendations to housing associations to reduce and possibly solve the gap between what they can do and what tenants want to happen. The main question of this thesis is: What can housing associations do to close the gap between them and their tenants in the social housing sector regarding combating energy poverty?<br/>This research will be carried out based on a qualitative study in which literature will be reviewed, and housing associations and tenant organisations will be interviewed. The aim is to identify the gap between what is desired by tenants and capable of housing associations and to draw up recommendations for housing associations to assist their tenants as well as possible. The recommendations of the research indicate that many of the gaps found during the comparison of the focus groups have to do with communication, both improving communication itself, and setting up communication between tenants and the association to reduce energy poverty.<br},
note = {Hoekstra, J.S.C.M. (mentor); Qian, QK (mentor); Croon, T.M. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Due to rising energy prices, an increasing number of households are experiencing difficulties with the affordability of their energy bills. As a result, households are unable to heat or cool their homes, or use electrical appliances as desired. This is known as energy poverty. This research focuses on energy poverty within housing associations. As two-thirds of households experiencing energy poverty live in housing association homes, this research is specifically targeted at housing associations. The research examines the possible gap between what housing associations are doing to combat energy poverty for their tenants, and what tenants would like to see housing associations do for them. Since renovation is simply too expensive and takes several years, it is excluded from consideration. As a result, housing associations will need to take other measures to help their tenants. This research will look at these taken measures and provides recommendations to housing associations to reduce and possibly solve the gap between what they can do and what tenants want to happen. The main question of this thesis is: What can housing associations do to close the gap between them and their tenants in the social housing sector regarding combating energy poverty?<br/>This research will be carried out based on a qualitative study in which literature will be reviewed, and housing associations and tenant organisations will be interviewed. The aim is to identify the gap between what is desired by tenants and capable of housing associations and to draw up recommendations for housing associations to assist their tenants as well as possible. The recommendations of the research indicate that many of the gaps found during the comparison of the focus groups have to do with communication, both improving communication itself, and setting up communication between tenants and the association to reduce energy poverty.<br |
Luc Ridder Improving DRL Of Vision-Based Navigation By Stereo Image Prediction (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Wu, Y. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:ef354713-924e-4907-a44f-95b67efa638e,
title = {Improving DRL Of Vision-Based Navigation By Stereo Image Prediction},
author = {Luc Ridder},
url = {http://resolver.tudelft.nl/uuid:ef354713-924e-4907-a44f-95b67efa638e},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate the effects of learning how to synthesize one view from the other in a stereo-vision setup without relying on any preliminary knowledge of the camera extrinisics and how it can be integrated for its downstream use for an obstacle avoidance task. As such, the literature study concludes that competitive geometry-free monocular-to-stereo image view synthesis is feasible due to recent developments in computer vision. The scientific paper further develops concepts proposed in the literature study and benchmarks the proposed architectures on depth estimation benchmarks for KITTI. Competitive results are achieved for view synthesis and despite sub-optimal performance compared to state-of-the-art monocular depth estimation, an ability to encode depth and detect shapes is present and, therefore, satisfactory for the application to DRL. Additionally, the research examines the benefits of using the latent space of a view synthesis architecture compared to other feature extractor methods as an input to the PPO agent implemented as auxiliary tasks. This method achieves quicker convergence and better performance for an obstacle avoidance task in a simulated indoor environment than the autoencoding feature extractor and end-to-end DRL methods. It is only outperformed by the monocular depth estimation feature extractor method. Overall, this research provides valuable insights for developing more efficient and effective DRL methods for monocular camera-based drones. Finally, the complementary code for this research can be found: urlhttps://github.com/ldenridder/drl-obstacle-avoidance-view-synthesis.},
note = {de Croon, G.C.H.E. (mentor); Wu, Y. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate the effects of learning how to synthesize one view from the other in a stereo-vision setup without relying on any preliminary knowledge of the camera extrinisics and how it can be integrated for its downstream use for an obstacle avoidance task. As such, the literature study concludes that competitive geometry-free monocular-to-stereo image view synthesis is feasible due to recent developments in computer vision. The scientific paper further develops concepts proposed in the literature study and benchmarks the proposed architectures on depth estimation benchmarks for KITTI. Competitive results are achieved for view synthesis and despite sub-optimal performance compared to state-of-the-art monocular depth estimation, an ability to encode depth and detect shapes is present and, therefore, satisfactory for the application to DRL. Additionally, the research examines the benefits of using the latent space of a view synthesis architecture compared to other feature extractor methods as an input to the PPO agent implemented as auxiliary tasks. This method achieves quicker convergence and better performance for an obstacle avoidance task in a simulated indoor environment than the autoencoding feature extractor and end-to-end DRL methods. It is only outperformed by the monocular depth estimation feature extractor method. Overall, this research provides valuable insights for developing more efficient and effective DRL methods for monocular camera-based drones. Finally, the complementary code for this research can be found: urlhttps://github.com/ldenridder/drl-obstacle-avoidance-view-synthesis. |
Koen Engelen Aerobatic maneuvering of Autonomous Hybrid UAVs: Trajectory Tracking using INDI in the Control Frame (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:2557c822-2360-4d2c-a6e9-0e05182c5c15,
title = {Aerobatic maneuvering of Autonomous Hybrid UAVs: Trajectory Tracking using INDI in the Control Frame},
author = {Koen Engelen},
url = {http://resolver.tudelft.nl/uuid:2557c822-2360-4d2c-a6e9-0e05182c5c15},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Unmanned Aerial Vehicles (UAVs) are increasingly being used in various applications, which demand longer endurance, extended range, and high maneuverability. These requirements necessitate the development of effective control methods for Hybrid UAVs. In this paper, we propose an outer loop Incremental Nonlinear Dynamic Inversion (INDI) controller for Hybrid UAVs, based on an analytically derived control effectiveness to control the linear acceleration of the UAV. The control effectiveness is derived in a new frame that does not show singularities, technically allowing controlled flight at all attitudes. For trajectory tracking purposes, a Proportional Derivative (PD) controller is added. In simulation the proposed controller shows comparable results to already existing INDI controllers for hover and forward flight. When performing loop the loops it is shown that the proposed control system is able to handle high roll angles, while the already existing INDI controller crashed.},
note = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Unmanned Aerial Vehicles (UAVs) are increasingly being used in various applications, which demand longer endurance, extended range, and high maneuverability. These requirements necessitate the development of effective control methods for Hybrid UAVs. In this paper, we propose an outer loop Incremental Nonlinear Dynamic Inversion (INDI) controller for Hybrid UAVs, based on an analytically derived control effectiveness to control the linear acceleration of the UAV. The control effectiveness is derived in a new frame that does not show singularities, technically allowing controlled flight at all attitudes. For trajectory tracking purposes, a Proportional Derivative (PD) controller is added. In simulation the proposed controller shows comparable results to already existing INDI controllers for hover and forward flight. When performing loop the loops it is shown that the proposed control system is able to handle high roll angles, while the already existing INDI controller crashed. |
Tom Suys Autonomous Control for Orographic Soaring of Fixed-Wing UAVs (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Remes, B.D.W. (mentor); Hwang, S. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:9b27d79d-d876-466d-b980-562c03552e6b,
title = {Autonomous Control for Orographic Soaring of Fixed-Wing UAVs},
author = {Tom Suys},
url = {http://resolver.tudelft.nl/uuid:9b27d79d-d876-466d-b980-562c03552e6b},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Prolonging the endurance of fixed-wing UAVs is crucial for achieving complex missions, yet their limited battery life poses a significant challenge. In response, this research proposes a novel approach to extend the endurance of fixed-wing UAVs by enabling autonomous soaring in an orographic wind field. The goal of our research is to develop a controller that can identify feasible soaring regions and autonomously maintain position control without using any throttle. Soaring flight is desirable as it results in a low energy cost with zero throttle usage. However, without throttle usage, the longitudinal motion of the UAV is an under-actuated system, presenting control challenges. The concept of a target gradient line (TGL) is introduced as part of the control algorithm that addresses these challenges and autonomously finds the equilibrium soaring position where sink rate and updraft are in equilibrium. Experimental tests showed promising results, demonstrating the controller’s effectiveness in maintaining autonomous soaring flight in a non-static wind field. We also demonstrate a single degree of control freedom in the soaring position through manipulation of the TGL.},
note = {de Croon, G.C.H.E. (mentor); Remes, B.D.W. (mentor); Hwang, S. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Prolonging the endurance of fixed-wing UAVs is crucial for achieving complex missions, yet their limited battery life poses a significant challenge. In response, this research proposes a novel approach to extend the endurance of fixed-wing UAVs by enabling autonomous soaring in an orographic wind field. The goal of our research is to develop a controller that can identify feasible soaring regions and autonomously maintain position control without using any throttle. Soaring flight is desirable as it results in a low energy cost with zero throttle usage. However, without throttle usage, the longitudinal motion of the UAV is an under-actuated system, presenting control challenges. The concept of a target gradient line (TGL) is introduced as part of the control algorithm that addresses these challenges and autonomously finds the equilibrium soaring position where sink rate and updraft are in equilibrium. Experimental tests showed promising results, demonstrating the controller’s effectiveness in maintaining autonomous soaring flight in a non-static wind field. We also demonstrate a single degree of control freedom in the soaring position through manipulation of the TGL. |
Martijn Brummelhuis A Centralised Approach to Aerial Manipulation on Overhanging Surfaces (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Hamaza, S. (mentor); Smeur, E.J.J. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:fd5e484b-bdd4-42e7-8cdc-70de94462858,
title = {A Centralised Approach to Aerial Manipulation on Overhanging Surfaces},
author = {Martijn Brummelhuis},
url = {http://resolver.tudelft.nl/uuid:fd5e484b-bdd4-42e7-8cdc-70de94462858},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Aerial physical interaction opens the door for many operations at height to be automatised using aerial robots. This research presents a novel manipulator design mounted on a traditional quadrotor, which utilises both mechanical and software compliance to perform physical interaction on vertical walls and overhanging surfaces, such as those found under bridges. A centralised impedance control scheme allows direct control of the end-effector pose without needing separate modes for free-flight and contact. A spring-loaded prismatic joint provides passive compliance while doubling as a force-feedback for the impedance controller through measuring the spring displacement. Simulation and flight experiments prove the feasibility and robustness of this approach for exchanging high forces at height, with a total of 44 successful experiments carried out in four sets. An average maximum force of 5.66 N or 19.3% of the system's weight was achieved over one set of 11 experiments.},
note = {Hamaza, S. (mentor); Smeur, E.J.J. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Aerial physical interaction opens the door for many operations at height to be automatised using aerial robots. This research presents a novel manipulator design mounted on a traditional quadrotor, which utilises both mechanical and software compliance to perform physical interaction on vertical walls and overhanging surfaces, such as those found under bridges. A centralised impedance control scheme allows direct control of the end-effector pose without needing separate modes for free-flight and contact. A spring-loaded prismatic joint provides passive compliance while doubling as a force-feedback for the impedance controller through measuring the spring displacement. Simulation and flight experiments prove the feasibility and robustness of this approach for exchanging high forces at height, with a total of 44 successful experiments carried out in four sets. An average maximum force of 5.66 N or 19.3% of the system's weight was achieved over one set of 11 experiments. |
Sebastien Origer Guidance & Control Networks for Time-Optimal Quadcopter Flight (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Ferede, R. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:efa9ee47-b200-4a52-b61d-d2c8e5b6fb78,
title = {Guidance & Control Networks for Time-Optimal Quadcopter Flight},
author = {Sebastien Origer},
url = {http://resolver.tudelft.nl/uuid:efa9ee47-b200-4a52-b61d-d2c8e5b6fb78},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal ’bang-bang’ control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance & Control Networks by learning to take consecutive waypoints into account. We fly a 4×3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance & Control Networks offer.},
note = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Ferede, R. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance & Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal ’bang-bang’ control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance & Control Networks by learning to take consecutive waypoints into account. We fly a 4×3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance & Control Networks offer. |
Anish Jadoenathmisier Aerial Perching via Active Touch: Embodying Robust Tactile Grasping on Aerial Robots (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Hamaza, S. (mentor); de Croon, G.C.H.E. (graduation committee); Pool, D.M. (graduation committee); Bredenbeck, A. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:f7ec9e1c-15db-4982-b2fa-4ba0f51a5b91,
title = {Aerial Perching via Active Touch: Embodying Robust Tactile Grasping on Aerial Robots},
author = {Anish Jadoenathmisier},
url = {http://resolver.tudelft.nl/uuid:f7ec9e1c-15db-4982-b2fa-4ba0f51a5b91},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Aerial manipulators, characterized by their ability to actively engage with the environment, are gaining popularity for their versatility in performing diverse tasks.<br/>This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities.},
note = {Hamaza, S. (mentor); de Croon, G.C.H.E. (graduation committee); Pool, D.M. (graduation committee); Bredenbeck, A. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Aerial manipulators, characterized by their ability to actively engage with the environment, are gaining popularity for their versatility in performing diverse tasks.<br/>This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities. |
Huamin Liu Collaborative Payload Carrying with Multiple MAVs (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:bfad7b0e-f6db-49d0-8642-5e4fbc6e3861,
title = {Collaborative Payload Carrying with Multiple MAVs},
author = {Huamin Liu},
url = {http://resolver.tudelft.nl/uuid:bfad7b0e-f6db-49d0-8642-5e4fbc6e3861},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {The transportation of payloads utilizing multiple drones presents a promising application for lifting heavier loads that exceed the payload capacity of a single drone. However, the cable-suspended payload introduces significant challenges to the system, and this research area remains relatively unexplored. In this work, a novel solution for payload-carrying application is proposed. First, the dynamics of cable-suspended payload transportation using multiple quadrotors, taking into account the influence of drag forces on the quadrotors are studied. A nonlinear optimization is employed to control the payload while distributing the control effort required for manipulating the suspended load over the drones in the formation while ensuring both tension constraints and collision avoidance between drones in the formation. The feasible path commands for formation agents are computed from the optimization. One of the critical aspects for controlling such a system is the load-introduced force, which exhibits rapid and complex variations. To address this, an extended state observer is employed to estimate the load force, eliminating the need for a tension sensor. In pursuit of a robust framework, a formation reset strategy is also developed, allowing to maintain load tracking performance and ensure the safety of formation agents, even in the event of a malfunction in one of the drones. A series of simulations are conducted to validate the effectiveness and robustness against disturbance and suspension failure of the proposed strategy and controllers. Results demonstrate that the whole multi-lift system can handle external disturbances, model uncertainties regarding drone inertia, mass and load mass, as well as suspension failures.},
note = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The transportation of payloads utilizing multiple drones presents a promising application for lifting heavier loads that exceed the payload capacity of a single drone. However, the cable-suspended payload introduces significant challenges to the system, and this research area remains relatively unexplored. In this work, a novel solution for payload-carrying application is proposed. First, the dynamics of cable-suspended payload transportation using multiple quadrotors, taking into account the influence of drag forces on the quadrotors are studied. A nonlinear optimization is employed to control the payload while distributing the control effort required for manipulating the suspended load over the drones in the formation while ensuring both tension constraints and collision avoidance between drones in the formation. The feasible path commands for formation agents are computed from the optimization. One of the critical aspects for controlling such a system is the load-introduced force, which exhibits rapid and complex variations. To address this, an extended state observer is employed to estimate the load force, eliminating the need for a tension sensor. In pursuit of a robust framework, a formation reset strategy is also developed, allowing to maintain load tracking performance and ensure the safety of formation agents, even in the event of a malfunction in one of the drones. A series of simulations are conducted to validate the effectiveness and robustness against disturbance and suspension failure of the proposed strategy and controllers. Results demonstrate that the whole multi-lift system can handle external disturbances, model uncertainties regarding drone inertia, mass and load mass, as well as suspension failures. |
Federico Magri Modular Neural Network Navigation for Autonomous Nano Drone Racing (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Ferede, R. (mentor); Bahnam, S.A. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:34213dbf-32ad-4f8d-b0f0-ed398608d682,
title = {Modular Neural Network Navigation for Autonomous Nano Drone Racing},
author = {Federico Magri},
url = {http://resolver.tudelft.nl/uuid:34213dbf-32ad-4f8d-b0f0-ed398608d682},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In this study, we present a first step towards a cutting-edge software framework that will enable autonomous racing capabilities for nano drones. Through the integration of neural networks tailored for real-time operation on resource-constrained devices. A lightweight Convolutional Neural Network, with the Gatenet architecture, is adjusted for reduced computational demand and is successfully deployed on a GAP8 processor at a rate of 16$Hz$. This network provides gates' size and location data for the subsequent positioning algorithm. A second neural network, trained through reinforcement learning, governs the drone's guidance and control systems, demonstrating a remarkable rate of 167$Hz$ on an STM32F405 processor. The attitude rates and thrust outputted by this network are then fed to an attitude rate PID controller.<br/><br/>The research shows that state-of-the-art neural networks for drone racing can be deployed on nano drones, despite their limited processing power. Nonetheless, the study demonstrated specific limitations, such as the perception network's sensitivity to white pixels in the image reducing its effectiveness when light sources are present in the scene. These findings underscore the importance of dataset composition and the need for diverse training scenarios to enhance the neural network's generalizability and performance in real-world applications.},
note = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Ferede, R. (mentor); Bahnam, S.A. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In this study, we present a first step towards a cutting-edge software framework that will enable autonomous racing capabilities for nano drones. Through the integration of neural networks tailored for real-time operation on resource-constrained devices. A lightweight Convolutional Neural Network, with the Gatenet architecture, is adjusted for reduced computational demand and is successfully deployed on a GAP8 processor at a rate of 16$Hz$. This network provides gates' size and location data for the subsequent positioning algorithm. A second neural network, trained through reinforcement learning, governs the drone's guidance and control systems, demonstrating a remarkable rate of 167$Hz$ on an STM32F405 processor. The attitude rates and thrust outputted by this network are then fed to an attitude rate PID controller.<br/><br/>The research shows that state-of-the-art neural networks for drone racing can be deployed on nano drones, despite their limited processing power. Nonetheless, the study demonstrated specific limitations, such as the perception network's sensitivity to white pixels in the image reducing its effectiveness when light sources are present in the scene. These findings underscore the importance of dataset composition and the need for diverse training scenarios to enhance the neural network's generalizability and performance in real-world applications. |
Alexis Wissen Tilt-rotor Tailsitter Global Acceleration Control: Behavioural Cloning of a Nonlinear Model Predictive Controller (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Smeur, E.J.J. (mentor); Ma, Z. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:c56b290f-8c43-4649-8360-13b7652710e8,
title = {Tilt-rotor Tailsitter Global Acceleration Control: Behavioural Cloning of a Nonlinear Model Predictive Controller},
author = {Alexis Wissen},
url = {http://resolver.tudelft.nl/uuid:c56b290f-8c43-4649-8360-13b7652710e8},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Capable of both vertical take-off and landing and forward flight, tail-sitters are a versatile class of UAVs with a large range of potential applications. A variant of tailsitters using tilt-rotors instead of ailerons for pitch and roll control has been proposed to mitigate the reduced control authority at low to zero velocities. The control of the translational dynamics for this platform is uniquely challenging. The extended flight envelope requires the controller to be able to perform hover and forward flight which are two flight phases with very different dynamics. Additionally, the tilt-rotor mechanism used to control the system is highly nonlinear which adds to the challenge. This paper presents a novel acceleration controller using Nonlinear Model Predictive Control (NMPC) in addition to the use of behavioural cloning to mimic the NMPC using a feedforward neural network. It is shown that behavioural cloning does successfully transfer general flight characteristics but that the performance is degraded with respect to the NMPC. Additionally, a sensitivity analysis was performed to investigate the effects of improper parameter estimation on controller performance. The most interesting result from this analysis is the strong sensitivity of both controllers to changes in centre of gravity location and mass.},
note = {Smeur, E.J.J. (mentor); Ma, Z. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Capable of both vertical take-off and landing and forward flight, tail-sitters are a versatile class of UAVs with a large range of potential applications. A variant of tailsitters using tilt-rotors instead of ailerons for pitch and roll control has been proposed to mitigate the reduced control authority at low to zero velocities. The control of the translational dynamics for this platform is uniquely challenging. The extended flight envelope requires the controller to be able to perform hover and forward flight which are two flight phases with very different dynamics. Additionally, the tilt-rotor mechanism used to control the system is highly nonlinear which adds to the challenge. This paper presents a novel acceleration controller using Nonlinear Model Predictive Control (NMPC) in addition to the use of behavioural cloning to mimic the NMPC using a feedforward neural network. It is shown that behavioural cloning does successfully transfer general flight characteristics but that the performance is degraded with respect to the NMPC. Additionally, a sensitivity analysis was performed to investigate the effects of improper parameter estimation on controller performance. The most interesting result from this analysis is the strong sensitivity of both controllers to changes in centre of gravity location and mass. |
DARSHAN KALYANASUNDARAM Factor graphs for inventory label scanning in warehouse (Masters Thesis) TU Delft Mechanical, Maritime and Materials Engineering, 2023, (de Croon, G.C.H.E. (mentor); Alonso Mora, J. (mentor); Ozo, Michaël (mentor); Caesar, H.C. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:bd663e11-947f-4ae1-ae2d-860d44fba7af,
title = {Factor graphs for inventory label scanning in warehouse},
author = {DARSHAN KALYANASUNDARAM},
url = {http://resolver.tudelft.nl/uuid:bd663e11-947f-4ae1-ae2d-860d44fba7af},
year = {2023},
date = {2023-01-01},
school = {TU Delft Mechanical, Maritime and Materials Engineering},
abstract = {A key important part of a warehouse operation is to keep track of the products in the warehouse. Traditionally, handheld scanners are used to scan the products to perform a stock count. The advancements in robotics have paved the way for new technologies that can improve the scanning process. This work shows how prior knowledge and warehouse structure can be used to perform scanning operations. The method uses a localized camera in the warehouse whose estimates drift over time and the knowledge about the environment to estimate the correct location of the product using factor graphs. The proposed method shows by exploiting the structure of the warehouse the drift in the VIO(Visual Inertial Odometry) can be reduced and the position estimation of the location labels can be improved},
note = {de Croon, G.C.H.E. (mentor); Alonso Mora, J. (mentor); Ozo, Michaël (mentor); Caesar, H.C. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
A key important part of a warehouse operation is to keep track of the products in the warehouse. Traditionally, handheld scanners are used to scan the products to perform a stock count. The advancements in robotics have paved the way for new technologies that can improve the scanning process. This work shows how prior knowledge and warehouse structure can be used to perform scanning operations. The method uses a localized camera in the warehouse whose estimates drift over time and the knowledge about the environment to estimate the correct location of the product using factor graphs. The proposed method shows by exploiting the structure of the warehouse the drift in the VIO(Visual Inertial Odometry) can be reduced and the position estimation of the location labels can be improved |
Hajo Erwich GSL-Bench: High Fidelity Gas Source Localization Benchmarking (Masters Thesis) TU Delft Aerospace Engineering, 2023, (de Croon, G.C.H.E. (mentor); Duisterhof, B.P. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:ab98e53a-5931-4b78-98ec-f0cb6df20986,
title = {GSL-Bench: High Fidelity Gas Source Localization Benchmarking},
author = {Hajo Erwich},
url = {http://resolver.tudelft.nl/uuid:ab98e53a-5931-4b78-98ec-f0cb6df20986},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Gas Source Localization (GSL) is a challenging field of research within the robotics community. Existing methods vary widely and each has its own strengths and weaknesses. Existing GSL evaluations vary in environment size, wind conditions, and gas simulation fidelity, thereby complicating objective comparison between algorithms. They also lack photo-realistic rendering for the integration of obstacle avoidance. In this paper, we propose GSL-Bench, a benchmarking suite to evaluate the performance of GSL algorithms. GSL-Bench features high-fidelity graphics and gas simulation. Realism is further increased by simulating relevant gas and wind sensors. Scene generation is simplified with the introduction of AutoGDM+, capable of procedural environment generation, CFD and particle-based gas dispersion simulation. To illustrate GSL-Bench's capabilities, three algorithms are compared in six warehouse settings of increasing complexity: E. Coli, dung beetle and a random walker. Our results demonstrate GSL-Bench's ability to provide valuable insights into algorithm performance.},
note = {de Croon, G.C.H.E. (mentor); Duisterhof, B.P. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Gas Source Localization (GSL) is a challenging field of research within the robotics community. Existing methods vary widely and each has its own strengths and weaknesses. Existing GSL evaluations vary in environment size, wind conditions, and gas simulation fidelity, thereby complicating objective comparison between algorithms. They also lack photo-realistic rendering for the integration of obstacle avoidance. In this paper, we propose GSL-Bench, a benchmarking suite to evaluate the performance of GSL algorithms. GSL-Bench features high-fidelity graphics and gas simulation. Realism is further increased by simulating relevant gas and wind sensors. Scene generation is simplified with the introduction of AutoGDM+, capable of procedural environment generation, CFD and particle-based gas dispersion simulation. To illustrate GSL-Bench's capabilities, three algorithms are compared in six warehouse settings of increasing complexity: E. Coli, dung beetle and a random walker. Our results demonstrate GSL-Bench's ability to provide valuable insights into algorithm performance. |
Rinto Vries Application of Control Barrier Functions to Collision Free Model Predictive Control: Robust UAV Trajectories with MPC-CBF and Euclidean Signed Distance Fields (Masters Thesis) TU Delft Aerospace Engineering, 2023, (Smeur, E.J.J. (mentor); Horstink, Thomas (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:6320374a-b84d-4bbf-be48-10cee914b9e0,
title = {Application of Control Barrier Functions to Collision Free Model Predictive Control: Robust UAV Trajectories with MPC-CBF and Euclidean Signed Distance Fields},
author = {Rinto Vries},
url = {http://resolver.tudelft.nl/uuid:6320374a-b84d-4bbf-be48-10cee914b9e0},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical description of the environment. Building upon this, we propose combining MPC-CBF with Euclidean Signed Distance Fields (ESDFs), eliminating the need for such an analytical model of the environment. Notably, we extend this approach to a new field by applying it to Unmanned Aerial Vehicles (UAVs). Through simulations, we compare flown trajectories and noise robustness for distance constraints, first-order CBF constraints and second-order CBF constraints. First-order CBF constraints outperform distance constraints, excelling in path planning and noise resilience. Second-order CBF constraints face challenges due to numerical approximations of the hessian of the ESDF and stricter dependency on an accurate acceleration model, limiting their practicality for UAVs. The proposed control framework was tested by safely maneuvering an enterprise inspection drone around a Boeing 787-9 aircraft inside an aircraft hangar, confirming its effectiveness in collision avoidance and real-world scenarios.},
note = {Smeur, E.J.J. (mentor); Horstink, Thomas (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical description of the environment. Building upon this, we propose combining MPC-CBF with Euclidean Signed Distance Fields (ESDFs), eliminating the need for such an analytical model of the environment. Notably, we extend this approach to a new field by applying it to Unmanned Aerial Vehicles (UAVs). Through simulations, we compare flown trajectories and noise robustness for distance constraints, first-order CBF constraints and second-order CBF constraints. First-order CBF constraints outperform distance constraints, excelling in path planning and noise resilience. Second-order CBF constraints face challenges due to numerical approximations of the hessian of the ESDF and stricter dependency on an accurate acceleration model, limiting their practicality for UAVs. The proposed control framework was tested by safely maneuvering an enterprise inspection drone around a Boeing 787-9 aircraft inside an aircraft hangar, confirming its effectiveness in collision avoidance and real-world scenarios. |
Chris Bononi Bello The development of a distributed electric propulsion (DEP) noise model (Masters Thesis) TU Delft Aerospace Engineering; TU Delft Aircraft Noise and Climate Effects, 2023, (Snellen, M. (mentor); Yin, F. (graduation committee); Smeur, E.J.J. (graduation committee); Heblij, S. (mentor); de Haan, W (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:319f4f93-0590-4f9e-8029-2911f61db477,
title = {The development of a distributed electric propulsion (DEP) noise model},
author = {Chris Bononi Bello},
url = {http://resolver.tudelft.nl/uuid:319f4f93-0590-4f9e-8029-2911f61db477},
year = {2023},
date = {2023-01-01},
school = {TU Delft Aerospace Engineering; TU Delft Aircraft Noise and Climate Effects},
note = {Snellen, M. (mentor); Yin, F. (graduation committee); Smeur, E.J.J. (graduation committee); Heblij, S. (mentor); de Haan, W (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
|
Miscellaneous
|
Hang Yu; Guido C. H. E Croon; Christophe De Wagter AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors (Miscellaneous) 2023. @misc{2301.07430,
title = {AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors},
author = {Hang Yu and Guido C. H. E Croon and Christophe De Wagter},
url = {https://arxiv.org/abs/2301.07430},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Tim Burgers; Stein Stroobants; Guido Croon Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps (Miscellaneous) 2023. @misc{2309.12937,
title = {Evolving Spiking Neural Networks to Mimic PID Control for Autonomous Blimps},
author = {Tim Burgers and Stein Stroobants and Guido Croon},
url = {https://arxiv.org/abs/2309.12937},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sunyou Hwang; Bart D. W. Remes; Guido C. H. E. Croon AOSoar: Autonomous Orographic Soaring of a Micro Air Vehicle (Miscellaneous) 2023. @misc{2308.00565,
title = {AOSoar: Autonomous Orographic Soaring of a Micro Air Vehicle},
author = {Sunyou Hwang and Bart D. W. Remes and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2308.00565},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Tom Suys; Sunyou Hwang; Guido C. H. E. Croon; Bart D. W. Remes Autonomous Control for Orographic Soaring of Fixed-Wing UAVs (Miscellaneous) 2023. @misc{2305.13891,
title = {Autonomous Control for Orographic Soaring of Fixed-Wing UAVs},
author = {Tom Suys and Sunyou Hwang and Guido C. H. E. Croon and Bart D. W. Remes},
url = {https://arxiv.org/abs/2305.13891},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Dario Izzo; Emmanuel Blazquez; Robin Ferede; Sebastien Origer; Christophe De Wagter; Guido C. H. E. Croon Optimality Principles in Spacecraft Neural Guidance and Control (Miscellaneous) 2023. @misc{2305.13078,
title = {Optimality Principles in Spacecraft Neural Guidance and Control},
author = {Dario Izzo and Emmanuel Blazquez and Robin Ferede and Sebastien Origer and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2305.13078},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sebastien Origer; Christophe De Wagter; Robin Ferede; Guido C. H. E. Croon; Dario Izzo Guidance & Control Networks for Time-Optimal Quadcopter Flight (Miscellaneous) 2023. @misc{2305.02705,
title = {Guidance & Control Networks for Time-Optimal Quadcopter Flight},
author = {Sebastien Origer and Christophe De Wagter and Robin Ferede and Guido C. H. E. Croon and Dario Izzo},
url = {https://arxiv.org/abs/2305.02705},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Robin Ferede; Guido C. H. E. Croon; Christophe De Wagter; Dario Izzo End-to-end Neural Network Based Quadcopter control (Miscellaneous) 2023. @misc{2304.13460,
title = {End-to-end Neural Network Based Quadcopter control},
author = {Robin Ferede and Guido C. H. E. Croon and Christophe De Wagter and Dario Izzo},
url = {https://arxiv.org/abs/2304.13460},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Stein Stroobants; Christophe De Wagter; Guido C. H. E. Croon Neuromorphic Control using Input-Weighted Threshold Adaptation (Miscellaneous) 2023. @misc{2304.08778,
title = {Neuromorphic Control using Input-Weighted Threshold Adaptation},
author = {Stein Stroobants and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2304.08778},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Jason Yik; Korneel Van Berghe; Douwe Blanken; Younes Bouhadjar; Maxime Fabre; Paul Hueber; Denis Kleyko; Noah Pacik-Nelson; Pao-Sheng Vincent Sun; Guangzhi Tang; Shenqi Wang; Biyan Zhou; Soikat Hasan Ahmed; George Vathakkattil Joseph; Benedetto Leto; Aurora Micheli; Anurag Kumar Mishra; Gregor Lenz; Tao Sun; Zergham Ahmed; Mahmoud Akl; Brian Anderson; Andreas G. Andreou; Chiara Bartolozzi; Arindam Basu; Petrut Bogdan; Sander Bohte; Sonia Buckley; Gert Cauwenberghs; Elisabetta Chicca; Federico Corradi; Guido Croon; Andreea Danielescu; Anurag Daram; Mike Davies; Yigit Demirag; Jason Eshraghian; Tobias Fischer; Jeremy Forest; Vittorio Fra; Steve Furber; P. Michael Furlong; William Gilpin; Aditya Gilra; Hector A. Gonzalez; Giacomo Indiveri; Siddharth Joshi; Vedant Karia; Lyes Khacef; James C. Knight; Laura Kriener; Rajkumar Kubendran; Dhireesha Kudithipudi; Yao-Hong Liu; Shih-Chii Liu; Haoyuan Ma; Rajit Manohar; Josep Maria Margarit-Taulé; Christian Mayr; Konstantinos Michmizos; Dylan Muir; Emre Neftci; Thomas Nowotny; Fabrizio Ottati; Ayca Ozcelikkale; Priyadarshini Panda; Jongkil Park; Melika Payvand; Christian Pehle; Mihai A. Petrovici; Alessandro Pierro; Christoph Posch; Alpha Renner; Yulia Sandamirskaya; Clemens JS Schaefer; André Schaik; Johannes Schemmel; Samuel Schmidgall; Catherine Schuman; Jae-sun Seo; Sadique Sheik; Sumit Bam Shrestha; Manolis Sifalakis; Amos Sironi; Matthew Stewart; Kenneth Stewart; Terrence C. Stewart; Philipp Stratmann; Jonathan Timcheck; Nergis Tömen; Gianvito Urgese; Marian Verhelst; Craig M. Vineyard; Bernhard Vogginger; Amirreza Yousefzadeh; Fatima Tuz Zohora; Charlotte Frenkel; Vijay Janapa Reddi NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems (Miscellaneous) 2023. @misc{2304.04640,
title = {NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems},
author = {Jason Yik and Korneel Van Berghe and Douwe Blanken and Younes Bouhadjar and Maxime Fabre and Paul Hueber and Denis Kleyko and Noah Pacik-Nelson and Pao-Sheng Vincent Sun and Guangzhi Tang and Shenqi Wang and Biyan Zhou and Soikat Hasan Ahmed and George Vathakkattil Joseph and Benedetto Leto and Aurora Micheli and Anurag Kumar Mishra and Gregor Lenz and Tao Sun and Zergham Ahmed and Mahmoud Akl and Brian Anderson and Andreas G. Andreou and Chiara Bartolozzi and Arindam Basu and Petrut Bogdan and Sander Bohte and Sonia Buckley and Gert Cauwenberghs and Elisabetta Chicca and Federico Corradi and Guido Croon and Andreea Danielescu and Anurag Daram and Mike Davies and Yigit Demirag and Jason Eshraghian and Tobias Fischer and Jeremy Forest and Vittorio Fra and Steve Furber and P. Michael Furlong and William Gilpin and Aditya Gilra and Hector A. Gonzalez and Giacomo Indiveri and Siddharth Joshi and Vedant Karia and Lyes Khacef and James C. Knight and Laura Kriener and Rajkumar Kubendran and Dhireesha Kudithipudi and Yao-Hong Liu and Shih-Chii Liu and Haoyuan Ma and Rajit Manohar and Josep Maria Margarit-Taulé and Christian Mayr and Konstantinos Michmizos and Dylan Muir and Emre Neftci and Thomas Nowotny and Fabrizio Ottati and Ayca Ozcelikkale and Priyadarshini Panda and Jongkil Park and Melika Payvand and Christian Pehle and Mihai A. Petrovici and Alessandro Pierro and Christoph Posch and Alpha Renner and Yulia Sandamirskaya and Clemens JS Schaefer and André Schaik and Johannes Schemmel and Samuel Schmidgall and Catherine Schuman and Jae-sun Seo and Sadique Sheik and Sumit Bam Shrestha and Manolis Sifalakis and Amos Sironi and Matthew Stewart and Kenneth Stewart and Terrence C. Stewart and Philipp Stratmann and Jonathan Timcheck and Nergis Tömen and Gianvito Urgese and Marian Verhelst and Craig M. Vineyard and Bernhard Vogginger and Amirreza Yousefzadeh and Fatima Tuz Zohora and Charlotte Frenkel and Vijay Janapa Reddi},
url = {https://arxiv.org/abs/2304.04640},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Federico Paredes-Vallés; Jesse Hagenaars; Julien Dupeyroux; Stein Stroobants; Yingfu Xu; Guido Croon Fully neuromorphic vision and control for autonomous drone flight (Miscellaneous) 2023. @misc{2303.08778,
title = {Fully neuromorphic vision and control for autonomous drone flight},
author = {Federico Paredes-Vallés and Jesse Hagenaars and Julien Dupeyroux and Stein Stroobants and Yingfu Xu and Guido Croon},
url = {https://arxiv.org/abs/2303.08778},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Federico Paredes-Vallés; Kirk Y. W. Scheper; Christophe De Wagter; Guido C. H. E. Croon Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow (Miscellaneous) 2023. @misc{2303.05214,
title = {Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow},
author = {Federico Paredes-Vallés and Kirk Y. W. Scheper and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2303.05214},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Robin Ferede; Christophe De Wagter; Dario Izzo; Guido C. H. E. Croon End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight (Miscellaneous) 2023. @misc{2311.16948,
title = {End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight},
author = {Robin Ferede and Christophe De Wagter and Dario Izzo and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2311.16948},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Alessandro Mancinelli; Bart D. W. Remes; Guido C. H. E. Croon; Ewoud J. J. Smeur Unified incremental nonlinear controller for the transition control of a hybrid dual-axis tilting rotor quad-plane (Miscellaneous) 2023. @misc{2311.09185,
title = {Unified incremental nonlinear controller for the transition control of a hybrid dual-axis tilting rotor quad-plane},
author = {Alessandro Mancinelli and Bart D. W. Remes and Guido C. H. E. Croon and Ewoud J. J. Smeur},
url = {https://arxiv.org/abs/2311.09185},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
B. D. W. Remes Aeronautical vehicle and method of transitioning between flight modes for an aeronautical vehicle (Miscellaneous) 2023, (B64C, B64U). @misc{52e041a36b6248f286a33ca18389e05a,
title = {Aeronautical vehicle and method of transitioning between flight modes for an aeronautical vehicle},
author = {B. D. W. Remes},
url = {https://research.tudelft.nl/en/publications/aeronautical-vehicle-and-method-of-transitioning-between-flight-m},
year = {2023},
date = {2023-01-01},
note = {B64C, B64U},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Julius Rückin; Federico Magistri; Cyrill Stachniss; Marija Popović Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning (Miscellaneous) 2023. @misc{2312.04402,
title = {Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning},
author = {Julius Rückin and Federico Magistri and Cyrill Stachniss and Marija Popović},
url = {https://arxiv.org/abs/2312.04402},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Hao Hu; Sicong Pan; Liren Jin; Marija Popović; Maren Bennewitz Active Implicit Reconstruction Using One-Shot View Planning (Miscellaneous) 2023. @misc{2310.00685,
title = {Active Implicit Reconstruction Using One-Shot View Planning},
author = {Hao Hu and Sicong Pan and Liren Jin and Marija Popović and Maren Bennewitz},
url = {https://arxiv.org/abs/2310.00685},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sicong Pan; Liren Jin; Hao Hu; Marija Popović; Maren Bennewitz How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF? (Miscellaneous) 2023. @misc{2310.00684,
title = {How Many Views Are Needed to Reconstruct an Unknown Object Using NeRF?},
author = {Sicong Pan and Liren Jin and Hao Hu and Marija Popović and Maren Bennewitz},
url = {https://arxiv.org/abs/2310.00684},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
David Morilla-Cabello; Jonas Westheider; Marija Popovic; Eduardo Montijano Perceptual Factors for Environmental Modeling in Robotic Active Perception (Miscellaneous) 2023. @misc{2309.10620,
title = {Perceptual Factors for Environmental Modeling in Robotic Active Perception},
author = {David Morilla-Cabello and Jonas Westheider and Marija Popovic and Eduardo Montijano},
url = {https://arxiv.org/abs/2309.10620},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Tobias Zaenker; Julius Rückin; Rohit Menon; Marija Popović; Maren Bennewitz Graph-based View Motion Planning for Fruit Detection (Miscellaneous) 2023. @misc{2303.03048,
title = {Graph-based View Motion Planning for Fruit Detection},
author = {Tobias Zaenker and Julius Rückin and Rohit Menon and Marija Popović and Maren Bennewitz},
url = {https://arxiv.org/abs/2303.03048},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Liren Jin; Xieyuanli Chen; Julius Rückin; Marija Popović NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering (Miscellaneous) 2023. @misc{2303.01284,
title = {NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering},
author = {Liren Jin and Xieyuanli Chen and Julius Rückin and Marija Popović},
url = {https://arxiv.org/abs/2303.01284},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Jonas Westheider; Julius Rückin; Marija Popović Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning (Miscellaneous) 2023. @misc{2303.01150,
title = {Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning},
author = {Jonas Westheider and Julius Rückin and Marija Popović},
url = {https://arxiv.org/abs/2303.01150},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Julius Rückin; Federico Magistri; Cyrill Stachniss; Marija Popović An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping (Miscellaneous) 2023. @misc{2302.03347,
title = {An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping},
author = {Julius Rückin and Federico Magistri and Cyrill Stachniss and Marija Popović},
url = {https://arxiv.org/abs/2302.03347},
year = {2023},
date = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
2022
|
Journal Articles
|
Brett Stephens; Hai-Nguyen Nguyen; S. Hamaza; Mirko Kovac An Integrated Framework for Autonomous Sensor Placement With Aerial Robots (Journal Article) In: IEEE/ASME Transactions on Mechatronics, vol. 28, no. 1, pp. 38–49, 2022, ISSN: 1083-4435, (Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.). @article{9c95a351584c4d5c918bc7b2ffe981ea,
title = {An Integrated Framework for Autonomous Sensor Placement With Aerial Robots},
author = {Brett Stephens and Hai-Nguyen Nguyen and S. Hamaza and Mirko Kovac},
url = {https://research.tudelft.nl/en/publications/an-integrated-framework-for-autonomous-sensor-placement-with-aeri-2},
doi = {10.1109/TMECH.2022.3202116},
issn = {1083-4435},
year = {2022},
date = {2022-01-01},
journal = {IEEE/ASME Transactions on Mechatronics},
volume = {28},
number = {1},
pages = {38–49},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
note = {Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sven Pfeiffer; Veronica Munaro; Shushuai Li; Alessandro Rizzo; Guido C. H. E. Croon Three-dimensional relative localization and synchronized movement with wireless ranging (Journal Article) In: Swarm Intelligence, vol. 17, no. 1-2, pp. 147–172, 2022, ISSN: 1935-3812, (Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.). @article{e1cd4276e8ee4c9bb302d49094774c6e,
title = {Three-dimensional relative localization and synchronized movement with wireless ranging},
author = {Sven Pfeiffer and Veronica Munaro and Shushuai Li and Alessandro Rizzo and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/three-dimensional-relative-localization-and-synchronized-movement},
doi = {10.1007/s11721-022-00221-0},
issn = {1935-3812},
year = {2022},
date = {2022-01-01},
journal = {Swarm Intelligence},
volume = {17},
number = {1-2},
pages = {147–172},
publisher = {Springer},
note = {Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
C. Wang; S. Wang; G. C. H. E. Croon; S. Hamaza Embodied airflow sensing for improved in-gust flight of flapping wing MAVs (Journal Article) In: Frontiers In Robotics and AI, vol. 9, 2022, ISSN: 2296-9144. @article{5c6513c332d341fb8b8ea5e591a3218f,
title = {Embodied airflow sensing for improved in-gust flight of flapping wing MAVs},
author = {C. Wang and S. Wang and G. C. H. E. Croon and S. Hamaza},
url = {https://research.tudelft.nl/en/publications/embodied-airflow-sensing-for-improved-in-gust-flight-of-flapping-},
doi = {10.3389/frobt.2022.1060933},
issn = {2296-9144},
year = {2022},
date = {2022-01-01},
journal = {Frontiers In Robotics and AI},
volume = {9},
publisher = {Frontiers Media},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Whereas insects seem to control their flight effortlessly with the help of optical flow, robots that attempt to do the same run into problems. We propose as a solution a learning process that allows robots to see distances by means of visual appearance (colors, textures, shapes). Would it be possible that insects do the same?
