Publications

@mastersthesis{uuid:90916a92-95bc-44eb-889e-81555ddd494f,

title = {Microphones as Airspeed Sensors for Micro Air Vehicles},

author = {Momchil Makaveev},

url = {http://resolver.tudelft.nl/uuid:90916a92-95bc-44eb-889e-81555ddd494f},

year  = {2023},

date = {2023-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {This project proposes and evaluates a novel concept for an airspeed instrument aimed at small hybrid unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer formed over the vehicle’s body to its airspeed. The instrument consists of two microphones, flush mounted on the UAV’s nose cone, that capture the pseudo-sound caused by the coherent turbulent structures, and a micro-controller that processes the signals from the microphones and computes the airspeed. Dedicated models were constructed, using data obtained from wind tunnel and flight experiments, that take the power spectra of the microphones’ signals as an input and provide the airspeed as an output. The model structure is a feed-forward neural network with a single hidden layer, trained using a second-order gradient descent algorithm, following a supervised learning approach. The models were validated using only flight data, with the best one achieving a mean approximation error of 0.043 m/s and having a standard deviation of 1.039 m/s. It was also shown that the airspeed could be successfully predicted for a wide range of angles of attack, given that they are known, thus necessitating the vehicle to be equipped with a dedicated angle of attack sensor.},

note = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:f5c8a6b4-a43b-43f1-9a2d-72d850515369,

title = {Frustumbug: a 3D Mapless Stereo-Vision-based Bug Algorithm for Micro Air Vehicles},

author = {Ruben Meester},

url = {http://resolver.tudelft.nl/uuid:f5c8a6b4-a43b-43f1-9a2d-72d850515369},

year  = {2023},

date = {2023-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {We present a computationally cheap 3D bug algorithm for drones, using stereo vision. Obstacle avoidance is important, but difficult for robots with limited resources, such as drones. Stereo vision requires less weight and power than active distance measurement sensors, but typically has a limited Field of View (FoV). In addition, the stereo camera is fixed on the drone, preventing sensor movement. For obstacle avoidance, bug algorithms require few resources. We base our proposed algorithm, Frustumbug, on the Wedgebug algorithm, since this bug algorithm copes with a limited FoV. Since Wedgebug only focuses on 2D problems, the Local-epsilon-Tangent-Graph (LETG) is used to extend the path planning to 3D. Disparity images are obtained through an optimised stereo block matching algorithm. Obstacles are expanded in disparity space to obtain the configuration space. Furthermore, Frustumbug has an improved robustness to noisy range sensor data, and includes reversing, climbing and descending manoeuvres to avoid or escape local minima. The algorithm has been extensively tested with 225 flights in two challenging simulated environments, with a success rate of 96%. Here, 3.6% did not reach the goal and 0.4% collided. Frustumbug has been implemented on a 20 gram stereo vision system, and guides drones safely around obstacles in the real world, showing its potential for small drones to reach their targets fully autonomously.},

note = {de Croon, G.C.H.E. (mentor); van Dijk, Tom (mentor); de Wagter, C. (graduation committee); Verhoeven, C.J.M. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:3e922cc4-83a0-43aa-a223-a67e554d2e92,

title = {Deep Vision-based Relative Localisation by Monocular Drones},

author = {Raoul Mink},

url = {http://resolver.tudelft.nl/uuid:3e922cc4-83a0-43aa-a223-a67e554d2e92},

year  = {2023},

date = {2023-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Decentralised drone swarms need real time collision avoidance, thus requiring efficient, real time relative localisation. This paper explores different data inputs for vision based relative localisation. It introduces a novel dataset generated in \textit{Blender}, providing ground truth optic flow and depth. Comparisons to \textit{MPI Sintel}, an industry/research standard optic flow dataset, show it to be a challenging and realistic dataset. Two Deep Neural Network (DNN) architectures (YOLOv3 & U-Net) were trained on this data, comparing optic flow to colour images for relative positioning. The results indicate that using optic flow provides a significant advantage in relative localisation. The flow based YOLOv3 had an mAP of 48%, 9% better than the RGB based YOLOv3, and 23% better than its equivalent U-Net. Its IoU_{0.5} of 63% was also 14% better than the RGB based YOLOv3, and 51% than its equivalent U-Net. As an input, it generalises better than RGB, as test clips with variant drones show. For these variants, the optical flow based networks outperformed the RGB based networks by a factor of 10.},

note = {de Croon, G.C.H.E. (mentor); de Wagter, C. (graduation committee); Zarouchas, D. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:bffb47bf-5864-4b18-921b-588b3a664866,

title = {Computationally Efficient Control Allocation Using Active-Set Algorithms},

author = {Till Blaha},

url = {http://resolver.tudelft.nl/uuid:bffb47bf-5864-4b18-921b-588b3a664866},

year  = {2023},

date = {2023-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {An effective distribution of flight control commands over many aircraft actuators (engines, control surfaces, flaps, etc.) can be achieved with constrained optimisation. Active-Set methods solve these problems efficiently, but their computational time requirements are still prohibitive for aircraft with many actuators or slower digital flight control processors. This work shows how these methods can be improved in these regards, by updating the required matrix factorisations at lower computational costs, rather than solving a separate optimisation problem at every step of the iterative algorithm. Additionally, it is shown how the sparsity of the problem matrices can be exploited. Both open-loop simulations and flight tests have been performed, which show that worst-case timings for a 6-rotor multicopter UAV can be improved by 65% over a current Active-Set solver. Furthermore, methods are presented that remedy numerical stability issues occurring in micro-controller floating point arithmetic but introduce a small but measurable adverse effect on the flight behaviour.},

note = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@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}

}

@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. 
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}

}

@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}

}

@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
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}

}

@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. 
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.


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. 

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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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?
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.
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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}