Courses

@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}

}

@mastersthesis{uuid:4e100997-a5b3-4863-a312-4721296fcdba,

title = {Towards landing a deep-stalled flying-wing in a powered flat spin: a proof of concept},

author = {Matteo Barbera},

url = {http://resolver.tudelft.nl/uuid:4e100997-a5b3-4863-a312-4721296fcdba},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Flying-wings show great potential for a vast number of applications, in both commercial and military sectors, thanks to their long range and fast forward flight, but suffer due to their lack of vertical take-off and landing capabilities. This paper presents a proof of concept for a novel landing method for a conventional flying wing that does not introduce additional weight dedicated only to the landing phase, with the aim of controlling a deep-stalled flying-wing in a powered flat spin. Through cyclic actuation of the servo motors and elevons, lateral forces as well as moments can be generated to control the position and attitude of the rotation plane. A successful indoor experiment was performed with a modified Parrot Disco in a controlled environment. Outdoor tests, however, failed to replicate the indoor results due to additional challenges present in the real flight conditions. A number of key challenges were identified, and the insights gained in this research lay an initial foundation for future work on this topic.},

note = {de Wagter, C. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:1136170f-3c4b-43b8-8b43-09e1e52d3bfd,

title = {Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging},

author = {Changrui Liu},

url = {http://resolver.tudelft.nl/uuid:1136170f-3c4b-43b8-8b43-09e1e52d3bfd},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem that has not been satisfactorily solved. In this work, based on the range-based peer-to-peer RL using the ultra-wideband (UWB) ranging technique, we develop a novel UWB-based cooperative relative localization (CRL) solution which integrates the relative motion dynamics of each host-neighbor pair to build a unified dynamic model and takes the distances between the neighbors as bonus information. Observability analysis using differential geometry shows that the proposed CRL scheme can expand the observable subspace compared to other alternatives using only direct distances between the host agent and its neighbors. In addition, we apply the kernel-induced extended Kalman filter (EKF) to the CRL state estimation problem with the novel-designed Logarithmic-Versoria (LV) kernel to tackle heavy-tailed UWB noise. Sufficient conditions for the convergence of the fixed-point iteration involved in the estimation algorithm are also derived. Comparative Monte Carlo simulations demonstrate that the proposed CRL scheme combined with the LV-kernel EKF significantly improves the estimation accuracy owing to its robustness against both the measurement outliers and incorrect measurement covariance matrix initialization. Moreover, with the LV kernel, the estimation is still satisfactory when performing the fixed-point iteration only once for reduced computational complexity.},

note = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (mentor); Mazo, M. (graduation committee); de Wagter, C. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:baf5b7df-0e0f-45da-8b70-c7c95ead79b6,

title = {Attitude Control of a Tilt-rotor Tailsitter Micro Air Vehicle Using Incremental Control},

author = {Gervase Lovell-Prescod},

url = {http://resolver.tudelft.nl/uuid:baf5b7df-0e0f-45da-8b70-c7c95ead79b6},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {By combining the ability to hover with a wing for fast and efficient horizontal flight, hybrid unmanned aircraft extend the flight envelope and therefore mission capabilities of unmanned aircraft. However, this comes at a cost: increased complexity control-wise and being more susceptible to wind disturbances. This susceptibility to wind gusts is particularly problematic for tailsitters as during hovering and vertical flight their wing is perpendicular to horizontal wind disturbances, often leading to actuator saturation. This paper presents a novel tailsitter micro air vehicle with two leading edge tilting rotors serving as its only actuators. It is shown that thrust vectoring generates sufficient control moment generation alleviating actuator saturation. Incremental nonlinear dynamic inversion (INDI) is implemented for attitude control and is demonstrated to compensate for unmodeled forces and moments whilst only relying on actuator control effectiveness and knowledge of actuator dynamics.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:1caff7b3-5c17-4b80-abea-19c629ce6051,

title = {Verification & Validation of Focus of Expansion estimation algorithm employing event-based optic flow},

author = {Stefan Knoops},

url = {http://resolver.tudelft.nl/uuid:1caff7b3-5c17-4b80-abea-19c629ce6051},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Event based vision has recently attracted a lot of attention. High data rates and robustness to lighting variations make it a valid option for indoor navigation. The previously developed FAITH algorithm calculates a possible Focus of Expansion
area based on negative half-planes generated by optic flow and by employing a RANSAC search, a fast and reliable Focus of Expansion estimation can be performed. This paper builds upon this algorithm by verifying and validating the
algorithm, improving the derotation capabilities and optimising for computational efficiency. Compared to earlier work, a higher accuracy and an increased robustness are realised by improving the data handling. Simulator results show accuracies in the range of 2 to 5 degrees. Online testing on a drone shows accuracies of up to 5 degrees while obtaining calculation times of only
2 · 10−3s and rates of 140Hz. Comparing the method to an alternative shows higher accuracy and better suitability to normal flow. Further research may contribute to more stable results and explore different hardware solutions.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:574db806-6096-4600-9926-3d737d1ee7da,

title = {NanoFlowNet: Real-time optical flow estimation on a nano quadcopter},

author = {Rik Bouwmeester},

url = {http://resolver.tudelft.nl/uuid:574db806-6096-4600-9926-3d737d1ee7da},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Nano quadcopters are small, agile, and cheap platforms well suited for deployment in narrow, cluttered environments. Due to their limited payload, nano quadcopters are highly constrained in processing power, rendering conventional vision-based methods for autonomous navigation incompatible. Recent machine learning developments promise high-performance perception at low latency, while novel ultra-low power microcontrollers augment the visual processing power of nano quadcopters. In this work, we present NanoFlowNet, an optical flow CNN that, based on the semantic segmentation architecture STDC-Seg, achieves real-time dense optical flow estimation on edge hardware. We use motion boundary ground truth to guide the learning of optical flow, improving performance with zero impact on latency. Validation on MPI-Sintel shows the high performance of the proposed method given its constrained architecture. We implement the CNN on the ultra-low power GAP8 microcontroller and demonstrate it in an obstacle avoidance application on a 34 g Bitcraze Crazyflie nano quadcopter.},

note = {de Croon, G.C.H.E. (mentor); Paredes-Vallés, Federico (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:4e4e333d-643f-43b9-99cb-650d697f5baa,

title = {ADAPT: A 3 Degrees of Freedom Reconfigurable Force Balanced Parallel Manipulator for Aerial Applications},

author = {KARTIK SURYAVANSHI},

url = {http://resolver.tudelft.nl/uuid:4e4e333d-643f-43b9-99cb-650d697f5baa},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Mechanical, Maritime and Materials Engineering},

abstract = {In this work, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator with pantograph legs for spatial motions applied underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, mixed, planar without disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup.
The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications.},

note = {van der Wijk, V. (mentor); Hamaza, S. (graduation committee); Herder, J.L. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:66c34a84-5b47-49dd-b560-2836d9696e3c,

title = {Incremental Nonlinear Dynamic Inversion controller - structural vibration coupling: Study of the phenomenon and the existing solutions},

author = {Alessandro Collicelli},

url = {http://resolver.tudelft.nl/uuid:66c34a84-5b47-49dd-b560-2836d9696e3c},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Incremental Nonlinear Dynamics Inversion (INDI) flight controllers are sensor-based control systems, that are robust towards model uncertainty and with good disturbance rejection characteristics. These controllers show coupling effects in structural modes when implemented in specific flying vehicles with low-frequency structural motions. This paper investigates different INDI implementations, standard INDI, hybrid INDI, and notch filter placement in the INDI loop via simulation and flight tests on the Nederdrone. System identification of the structural characteristics of the vehicle and the system’s yaw dynamics are executed via ground vibration and hover flight tests. Closed-loop behaviour of theINDI inner-loop, disturbance rejection performance, and outer loop step-tracking performance was assessed with dedicated flight tests. The investigated INDI solutions show similar disturbance rejection and outer-loop step tracking performance, while the hybrid INDI solution performs a better nonlinear dynamic inversion. 
Index Terms—INDI, complementary filter, unmanned vehicle, flight control system structural motion coupling},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:6215dd57-8d16-466b-a286-341538675d2d,

title = {A Bio-inspired Sensing Approach to in-Gust Flight of Flapping Wing MAVs},

author = {Chenyao Wang},

url = {http://resolver.tudelft.nl/uuid:6215dd57-8d16-466b-a286-341538675d2d},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},

abstract = {Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case.},

note = {Hamaza, S. (mentor); de Croon, G.C.H.E. (mentor); Wang, S. (graduation committee); de Wagter, C. (graduation committee); van Oudheusden, B.W. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:05f743a5-39c8-4860-9976-1eee532184a9,

title = {Developing a modular tool to simulate regeneration power potential using orographic wind-hovering UAVs},

author = {Midas Gossye},

url = {http://resolver.tudelft.nl/uuid:05f743a5-39c8-4860-9976-1eee532184a9},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Applications of Unmanned Aerial Vehicles (UAV's) are often limited by flight endurance. To address the limitation of endurance, we propose a regenerative soaring method in this paper. The atmospheric energy from updrafts generated by obstacles such as hills and ships can be harvested by UAV's using a regenerative electric drivetrain. With fixed-wing aircraft, the vehicle can hover with specific wind conditions, and the battery can be recharged in the air while wind hovering. In order to research the feasibility of this regenerative soaring method, we present a model to estimate hovering locations and the amount of extractable power using the proposed method. The resulting modular regeneration simulation tool can efficiently determine the possible hovering locations and provide an estimate of the power regeneration potential for each hovering location, given the UAV's aerodynamic characteristics and wind-field conditions. Furthermore, a working regenerative drivetrain test setup was constructed and characterised that showcased promising conversion efficiencies and can be incorporated into existing UAV's easily.},

note = {Remes, B.D.W. (mentor); Hwang, S. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:41222049-fb57-4f26-9b9e-85939af9fa63,

title = {Sensorless Impedance Control for Curved Surface Inspections Using the Omni-Drone Aerial Manipulator},

author = {Hani Abu-Jurji},

url = {http://resolver.tudelft.nl/uuid:41222049-fb57-4f26-9b9e-85939af9fa63},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {In this thesis, we develop a novel aerial manipulator system with an omni-directional workspace. The system comprises of a quadrotor platform equipped with a rotating five-bar linkage and serves the purpose of demonstrating the ability to perform contour tracing tasks on complex shapes, whilst airborne. In order to remove the dependency on additional force sensors and keep the design lightweight, an onboard force estimation scheme is implemented based on the generalized momentum of the system, using the torque feedback from the manipulator's motors. The computed force estimate feeds in a position-based impedance controller with the purpose of maintaining continuous contact through the manipulator's end-effector as the system traces contours of unknown curved geometry. Results demonstrate the estimator's ability to track the applied forces, while the impedance controller shows adequate contour following. The preliminary results obtained on both stationery and flight experiments validate this approach and show potential for aerial contact inspections of more complex structures.},

note = {Hamaza, S. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:df815057-9ab6-42ee-8290-ce8099ffda68,

title = {Incremental Nonlinear Dynamic Inversion Controller for a Variable Skew Quad Plane},

author = {Tomaso De Ponti},

url = {http://resolver.tudelft.nl/uuid:df815057-9ab6-42ee-8290-ce8099ffda68},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {This paper presents the design of an Incremental Nonlinear Dynamic Inversion (INDI) controller for the novel platform VSQP. Part of the identified challenges is the develop- ment of a model for the actuator effectiveness and lift especially as a function of skew, the newly added degree of freedom. In particular it is assumed that the actuator effectiveness changes linearly with actuator state and that aerodynamic forces change quadratically with airspeed and depend mainly on the chordwise component of airspeed. Moreover, the position of the moving actuators is expressed as a function of the corresponding moment arm and the skew angle. The models and assumptions are verified through static and dynamic wind tunnel tests at the OJF of TuDelft. A WLS routine is used to solve the control allocation for the overactuated guidance loop. A lower cost is assigned to the use of the push motor so to steer the control allocation in its favor rather than commanding changes in attitude. A gradual switch of the hover motors in transition is achieved by scheduling the lift-pitch effectiveness with airspeed. Therefore, as airspeed increases the outerloop INDI controller evaluates that changing pitch to achieve a certain vertical acceleration set point results in an increasingly cheaper command allocation than changing thrust. An automatic skew controller is designed based on the developed control moment and lift models. The skew angle is scheduled with airspeed so to perform transition while also maximizing control authority. Finally, the controller is validated by performing multiple transitions inside the OJF windtunnel.},

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:fdd8e2fa-1372-4f79-aa05-6ab152e848e1,

title = {Adaptive control with Multivariate B-Splines and INDI: A case study for Vertical take-off and landing drones},

author = {Prawien Kanhai},

url = {http://resolver.tudelft.nl/uuid:fdd8e2fa-1372-4f79-aa05-6ab152e848e1},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {In recent years the popularity of VTOL (Vertical Take-Off and Landing) drones has increased significantly. Due to their hybrid design, these drones can take off and land vertically and fly horizontally, enabling them to land in difficult terrain and have a more extensive range than the Quadcopter counterpart. However, this hybrid design also introduces complex dynamics that are difficult to model. For adequate control, this requires an adaptive element that can compensate for the modeling errors. Due to the significant change in flight conditions, adaptations must be made effectively over the entire flight envelope of a VTOL drone. This thesis introduces an adaptive controller that can cope with the large flight envelope and varying flight conditions of the VTOL drone and can adapt the controller effectively and store previous adaptations with multivariate B-splines during real-time flights.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:b7070c31-9db1-4a0c-8605-fb871914501b,

title = {UWB Localisation: Distributed UWB inter-ranging for MAV swarms in large GNSS-denied environments},

author = {Fréderic Dupon},

url = {http://resolver.tudelft.nl/uuid:b7070c31-9db1-4a0c-8605-fb871914501b},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {The use of micro air vehicles (MAV) is becoming increasingly mainstream and with them their applications have become more demanding across the board. The application of MAV’s in large GNSS-denied environments often asks for a distributed and scalable localisation system with minimal reliance on static localisation hardware. In this research a distributed ultra-wideband (UWB) localisation system that takes advantage of the collaborative capabilities of a swarm of MAV’s has been developed and tested in both simulation and practice. Additionally, a modular UWB simulator has been developed which enables researchers to test UWB localisation schemes for a swarm of MAV’s. It has been found that when taking advantage of the UWB inter-agent ranging capabilities of a swarm of micro air vehicles, one can increase the coverage of an UWB setup in spaces with coverage-issues and conversely increase the accuracy of an existing UWB setup that has full UWB coverage.},

note = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:41895fac-aa59-47db-9c01-5e2879460b57,

title = {Model and Actuator Based Trajectory Tracking for Incremental Nonlinear Dynamic Inversion Controllers},

author = {Pietro Campolucci},

url = {http://resolver.tudelft.nl/uuid:41895fac-aa59-47db-9c01-5e2879460b57},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:be142c0a-3475-4571-b9c5-9118d397c51a,

title = {Onboard Drone Detection with Event Cameras},

author = {Alejandro Barberia Chueca},

url = {http://resolver.tudelft.nl/uuid:be142c0a-3475-4571-b9c5-9118d397c51a},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {In an effort to develop a new relative sensing method for drone swarms, the suitability of event cameras is assessed for propeller detection. Benchmark tests were conducted for different propellers under different lighting and background conditions, varying the observation distance and spinning frequency. The different tests were evaluated on event count, frequency, and clustering, as these are the most characteristic properties of the propeller-generated signal. A propeller detection metric was derived as a fuzzy classifier to assess detectability. It was observed that the sensor employed is limiting the detection range due to low resolution, with a maximum detection range of 75 cm. While at low spinning frequencies it is possible to detect the propeller at such distance, for higher frequences (6000 to 8000 RPMs) the range decreases to 45 cm for the tests with highest blade to background contrast and two-blade propellers. It was observed that lower contrasts reduce the successful detections only to low frequencies, and three-blade propellers become completely indetectable due to the static overlap between the blades. Therefore, it is concluded that, at this stage of the technology, the use case of event cameras for relative sensing is constrained to close distances with high contrast.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b,

title = {Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation},

author = {Yvonne Eggers},

url = {http://resolver.tudelft.nl/uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates.},

note = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c,

title = {An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers},

author = {Robin Ferede},

url = {http://resolver.tudelft.nl/uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Developing optimal controllers for aggressive high speed quadcopter flight remains a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning are a good candidate for real-time optimal quadcopter control. In these methods, the networks (termed G&CNets) are trained using optimal trajectories obtained from a dynamical model of the quadcopter by means of a direct transcription method. A major problem with these methods is the effects of unmodeled dynamics. In this work we identify these effects for G&CNets trained for power optimal full state-to-rpm feedback. We propose an adaptive control strategy to mitigate the effects of unmodeled roll, pitch and yaw moments. Our method works by generating optimal trajectories with constant external moments added to the model and training a network to learn the policy that maps state and external moments to the corresponding optimal rpm command. We demonstrate the effectiveness of our method by performing power-optimal hover-to-hover flights with and without moment feedback. The flight tests show that the inclusion of this moment feedback significantly improves the controller's performance. Additionally we compare the adaptive controller's performance to a time optimal Bang-Bang controller for consecutive waypoint flight and show significantly faster lap times on a 3x4m track.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca,

title = {Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation?},

author = {Jan Verheyen},

url = {http://resolver.tudelft.nl/uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300,

title = {An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies},

author = {Erik Oever},

url = {http://resolver.tudelft.nl/uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29,

title = {We fly as one: Design and Joint Control of a Conjoined Biplane and Quadrotor},

author = {Shawn Schröter},

url = {http://resolver.tudelft.nl/uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Unmanned Aerial Vehicles, UAVs, serve many purposes
these days, such as short-range inspections
and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.

This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.

This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight
release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:
a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.
In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies
are of minimal influence. However, the angular rate damper
strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.
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:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6,

title = {Evolving-to-Learn with Spiking Neural Networks},

author = {Jingyi LU},

url = {http://resolver.tudelft.nl/uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This thesis aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. In this work, we first test the algorithm in basic binary pattern classification tasks. Then, using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:7735d01c-b4cd-4173-a584-652f269c078c,

title = {Semantic Segmentation using Deep Neural Networks for MAVs},

author = {Tommy Tran},

url = {http://resolver.tudelft.nl/uuid:7735d01c-b4cd-4173-a584-652f269c078c},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6,

title = {Grammatical Evolution for Optimising Drone Behaviors},

author = {Chris Groen},

url = {http://resolver.tudelft.nl/uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors.},

note = {Li, S. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:a4305f40-c095-45c2-bc7e-84e23efa70d6,

title = {Self-supervised finetuning of stereo matching algorithms},

author = {Roelof Stikker},

url = {http://resolver.tudelft.nl/uuid:a4305f40-c095-45c2-bc7e-84e23efa70d6},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Abstract— Stereo vision is a commonly applied method to achieve depth perception on Micro Air Vehicles (MAVs). Stereo matching algorithms are often optimized for specific environments and camera properties, using the ground truth error as a supervisor. However, in practical applications ground truth data is usually not available. Therefore, in this research, we finetune several conventional stereo matching algorithms (BM, SGBM, and ELAS) and a neural network (AnyNet) using self-supervision. The settings of the conventional algorithms are optimized with NSGA-II, using the reconstruction error and disparity density as objective functions. AnyNet is finetuned with the reconstruction error, as well as with the disparity map of conventional methods. We conclude that finetuning the parameters of conventional stereo algorithms using the reconstruction error can lead to a slight improvement in performance compared with the general settings, depending on the stereo algorithm. The performance of the conventional methods is comparable to that of AnyNet on a major portion of the image. However, removing the values with low confidence in the disparity map of ELAS and interpolating the missing disparities leads to an accuracy well above AnyNet.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:bb6ace70-512e-4e0e-a834-6b065ece52c5,

title = {Scalable Positioning Method for MAV Localisation using Two onboard UWB Tags},

author = {Bas Beurden},

url = {http://resolver.tudelft.nl/uuid:bb6ace70-512e-4e0e-a834-6b065ece52c5},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Abstract—Ultra-wideband (UWB) ranging is a very suitable method for indoor localisation of unmanned aerial vehicles (UAVs). Current solutions of UWB ranging however either focus on achieving a high accuracy or focus on scalability. In this research a positioning algorithm for UAVs is presented that combines high accuracy performance with a high level of system scalability. The localisation method uses commercially available off the shelf components and is implemented by connecting two UWB sensors to a micro aerial vehicle. From
both sensors, time-difference of arrival (TDOA) measurements were collected during flights and additionally, a tag-TDOA between the two UWB sensors was measured which estimates the angle-of-arrival of the incoming signals. It was found that state estimation using TDOA measurements from both UWB sensors has a reduced positioning error compared to the algorithm using TDOA measurements from one UWB sensor, without significantly affecting yaw estimation accuracy. Furthermore, the tag-TDOA measurement did not improve the estimation accuracy at the implemented baseline of 0.22 metres as the
measurement error was too large compared to the baseline.},

note = {Pfeiffer, S.U. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9,

title = {Evolved Neuromorphic Altitude Controller for an Autonomous Blimp},

author = {Marina Gonzalez Alvarez},

url = {http://resolver.tudelft.nl/uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III.},

note = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Corradi, Federico (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:0f908624-ddf3-4329-817e-3170d2b6b656,

title = {Thermistor-based airflow sensing on a flapping wing micro air vehicle},

author = {Sunyi Wang},

url = {http://resolver.tudelft.nl/uuid:0f908624-ddf3-4329-817e-3170d2b6b656},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Flow sensing exists widely in nature to help animals perform certain tasks. It has also been widely adopted in engineering applications with different types of sensing instrumentation. In particular, in the field of aerospace engineering, airflow sensing is crucial to vehicle state evaluation and flight control. This project surveys the key mechanisms from biological features in nature that enable flow sensing and expands towards the application motivation to identify a suitable airflow sensor that can be equipped to a flapping wing micro air vehicle (FWMAV) for onboard airflow sensing. 

The selection of sensors is first narrowed down to three major types of airflow sensors from the state of art that have the most potential to be integrated onboard a flapping wing MAV, considering the sensor performance need, size, weight and power (SWaP) restrictions. Two thermal-based commercially available low-cost airflow sensors RevP and RevC from Modern Device have been selected after the trade-off analysis. 

A full workflow of calibrating and evaluating the two airflow sensors' directional sensitivity has been carried out through two wind tunnel campaigns. Their performance under grid-generated turbulence is compared with a constant temperature hot-wire anemometer. This series of tests leads to the conclusion that the RevP airflow sensor has better performance and is therefore chosen to be placed onboard a flapping wing MAV Delfly Nimble. 

Both mounted tests and tethered hovering tests with the Delfly Nimble are performed to further examine the airflow sensor RevP's measurement performance under different influence factors such as MAV throttle levels, MAV body pitch angles and freestream speeds. In the end, it is concluded that as a proof of concept, the RevP sensor is capable of performing effective measurements for low flow speeds less than 4 m/s, within the pitching angle range of -30 to 30 degrees. Although this is the first achieved tethered hover flight with onboard airflow sensing for a flapping wing MAV, its limited payload and onboard power supply demands an even smaller and less power consuming design of airflow sensors to enable further applications such as autonomous reactive control under wind disturbances.},

note = {van Oudheusden, B.W. (mentor); de Croon, G.C.H.E. (graduation committee); Olejnik, D.A. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:d3baec43-71d4-4f7f-ae27-2fdfdae7fea3,

title = {End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options},

author = {Zhouxin Ge},

url = {http://resolver.tudelft.nl/uuid:d3baec43-71d4-4f7f-ae27-2fdfdae7fea3},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy Option Critic (PPOC) is an end-to-end hierarchical reinforcement learning method that alleviates the need for a high-fidelity flight model and allows for adaptive flight control. This research contributes to the development and analysis of online adaptive flight control by comparing PPOC against a non-hierarchical method called Proximal Policy Optimization (PPO) and PPOC with a single Option (PPOC-1). The methods are tested on an extendable mass-spring-damper system and aircraft model. Subsequently, the agents are evaluated by their sample efficiency, reference tracking capability and adaptivity. The results show, unexpectedly, that PPO and PPOC-1 are more sample efficient than PPOC. Furthermore, both PPOC agents are able to successfully track the height profile, though the agents learn a policy that results in noisy actuator inputs. Finally, PPOC with multiple learned Options has the best adaptivity, as it is able to adapt to structural failure of the horizontal tailplane, sign change of pitch damping, and generalize to different aircraft.},

note = {van Kampen, E. (mentor); de Croon, G.C.H.E. (graduation committee); Mitici, M.A. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:b18cead1-951b-4a21-a4bb-0ba36f1768ee,

title = {Position controller for a flapping-wing drone using ultra wide band},

author = {Guillermo Gonzalez Archundia},

url = {http://resolver.tudelft.nl/uuid:b18cead1-951b-4a21-a4bb-0ba36f1768ee},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {The continuous improvement and miniaturisation of elements in drones have been essential for making flapping-wing drones a reality. This thesis presents an integral approach for accurate indoor position control and estimation on flapping-wing drones. The approach considers three main aspects to enhance transient response of the drone. The first one is an experimental velocity/attitude flapping-wing model for drag compensation, obtained through system identification techniques. The second one is a voltage-dependent variable thrust model for enhancing height control. Thirdly, a characterisation of ground effects to determine the height for stable hovering. For the state estimation, an extended Kalman filter fuses UWB position measurements with IMU data. Due to the well-known multi-path effects of UWB, the Kalman filter includes an adaptive noise parameter based on height. The novel control strategy was validated with real flight tests, where position control improved by a factor of 1.5, reaching a mean absolute error of 10cm in positions in x and y, and 4.9cm for position in z.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}