Publications

@mastersthesis{uuid:15f2daf8-1596-4514-8b1b-e139881cfaf3,

title = {An intelligent leader-follower neural controller in adverse observability scenarios},

author = {Eduardo Falcão da Cruz Rodrigues Lourenço},

url = {http://resolver.tudelft.nl/uuid:15f2daf8-1596-4514-8b1b-e139881cfaf3},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {A high-level neural controller for leader-follower flight is presented. State of the art range-based relative localization schemes that rely exclusively on onboard sensors present an additional challenge to the leader-follower control problem since they restrict the flight conditions that guarantee observability. This novel controller was developed over an evolutionary process in which the simulation environment resembled the real-life constraints a group of MAVs would encounter. During the learning stage, a group of three agents is used, where one acts as a leader and flies a random trajectory, and the other two act as followers guided by a candidate controller that dictates the desired velocity commands. In the end, when equipped with the best-evolved controller, the follower agents are able to showcase a successful following behaviour that also enhances the observability of the system, although no observability metric was included in evolution.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:302426ff-a4a4-4f8a-967a-331ea71b1ba1,

title = {AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors},

author = {Rano Veder},

url = {http://resolver.tudelft.nl/uuid:302426ff-a4a4-4f8a-967a-331ea71b1ba1},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we present AvoidBench, a benchmarking suite capable of evaluating the performance of vision-based obstacle avoidance algorithms for multi-rotors in simulation. Utilising a set of performance metrics, AvoidBench assigns performance scores to obstacle avoidance algorithms by subjecting them to a series of tasks. Using both Airsim and Unreal engine under the hood, we are able to provide high-fidelity visuals and dynamics, leading to a relatively small gap between simulation and reality. AvoidBench comes included with a simple, but powerful C++ and Python API which provides functionality for procedural environment generation, custom benchmark design, and an easy-to-use framework for users to implement their own vision-based obstacle avoidance methods. Implementing an obstacle avoidance method can be done entirely in a single file, allowing anyone to share and compare their obstacle detection and avoidance algorithms with others.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:7dd7edc9-0037-4c3e-a667-aac7476f272f,

title = {Sniffy Bug: A fully autonomous and collaborative swarm of gas-seeking nano quadcopters in cluttered environments},

author = {Bart Duisterhof},

url = {http://resolver.tudelft.nl/uuid:7dd7edc9-0037-4c3e-a667-aac7476f272f},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Nano quadcopters are ideal for gas source localization (GSL) as they are cheap, safe and agile. However, previous algorithms are unsuitable for nano quadcopters, as they rely on heavy sensors, require too large computational resources, or only solve simple scenarios without obstacles. In this work, we propose a novel bug algorithm named `Sniffy Bug', that allows a swarm of gas-seeking nano quadcopters to localize a gas source in an unknown, cluttered and GPS-denied environment. Sniffy Bug is capable of efficient GSL with extremely little sensory input and computational resources, operating within the strict resource constraints of a nano quadcopter. The algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based procedure. We evolve all the parameters of the bug (and PSO) algorithm, with a novel automated end-to-end simulation and benchmark platform, AutoGDM. This platform enables fully automated end-to-end environment generation and gas dispersion modelling (GDM), not only allowing for learning in simulation but also providing the first GSL benchmark. We show that evolved Sniffy Bug outperforms manually selected parameters in challenging, cluttered environments in the real world. To this end, we show that a lightweight and mapless bug algorithm can be evolved to complete a complex task, and enable the first fully autonomous swarm of collaborative gas-seeking nano quadcopters.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:ada45454-c19a-4fba-b842-62efd2320a6a,

title = {System Identification of the Delfly Nimble: Modeling of the Lateral Body Dynamics},

author = {Karan Bains},

url = {http://resolver.tudelft.nl/uuid:ada45454-c19a-4fba-b842-62efd2320a6a},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Flapping wing micro air vehicles (FWMAV's) are a subcategory of unmanned aerial vehicle which use flapping wings for thrust generation. The high agility and maneuverability of FWMAV's are very favorable attributes, making them more applicable in cluttered spaces. A tailless FWMAV called the Delfly Nimble has been developed at the Delft University of Technology. Due to the inherent instability of the tailless design an active controller is required to ensure safe and stable flight of the drone. In previous research, models have been developed for the longitudinal dynamics of the Delfly Nimble. In this paper, a grey-box state-space model of the lateral body dynamics in hover conditions is identified using system identification techniques. The parameters which needed to be estimated were stability and control derivatives, and they were obtained with a least-squares approach. Free-flight experiments were performed to generate the identification and validation data. A doublet train was used in the identification experiments, with the gains of the controller adjusted in such a way that maximum excitation was acquired. The identified model has been validated with various maneuvers. These included doublets, 112-maneuvers, maneuvers using coupled inputs, and maneuvers with sideways flight. The resulting model is able to predict the state derivatives of most maneuver accurately, reaching accuracies of over 90% for maneuvers close to hover. Moreover, in closed-loop configuration it is able to simulate the state response accurately, with accuracies of over 85% for maneuvers close to hover, and remains stable, making it applicable for controller design and stability analysis. Finally, based on the model the inherent instability of the lateral body dynamics was also confirmed, for there are eigenvalues with positive real parts.},

note = {de Visser, C.C. (mentor); Olejnik, D.A. (mentor); Karasek, M. (mentor); Armanini, S.F. (mentor); de Croon, G.C.H.E. (graduation committee); Mooij, E. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:b2db7412-74d9-4914-94b7-0b922a061adc,

title = {Hybrid UAV Attitude Control using INDI and Dynamic Tilt-Twist},

author = {Lars Dellemann},

url = {http://resolver.tudelft.nl/uuid:b2db7412-74d9-4914-94b7-0b922a061adc},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {The application of Unmanned Aerial Vehicles (UAVs) is increasing, much like the performance of these aircraft. A tailsitter is a type of UAV which is capable of performing vertical take-offs and landings (VTOL) and long endurance flights. During hover, the yaw control is limited due to the dynamics of these tailsitters. The generally used quaternion feedback for the attitude does not compensate for this as it describes a singular rotation. Tilt-twist is a solution to the problem as it splits the tilt (pitch and roll) from the twist (yaw). The axis of the yaw rotation is body fixed. When hovering with a pitch and/or roll angle the twist axis will be aligned with the body z-axis, instead of the desired gravitational force vector (for position control). Previous tilt-twist methods used a PID controller. This paper describes an improvement over previous tilt-twist approaches, the dynamic tilt-twist in combination with INDI. The INDI controller is designed for nonlinear systems. The dynamic tilt-twist compensates for the problem with the normal tilt-twist as test results will demonstrate. Tests are performed in a simulation and a real life test with the NederDrone hybrid tailsitter is done.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:054d38f7-8fe7-447c-b9fc-9d13437fcef5,

title = {Acoustic Perception in Intelligent Vehicles using a single microphone system},

author = {Avinash Mattar},

url = {http://resolver.tudelft.nl/uuid:054d38f7-8fe7-447c-b9fc-9d13437fcef5},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics; TU Delft Intelligent Vehicles},

abstract = {Passive acoustic sensing utilizes the ability of sound to travel beyond the line-of-sight to understand the surroundings. This provides an advantage over the currently used sensors in Intelligent Vehicles that can sense obstacles within their line-of-sight only. Recently, a localization based approach has been implemented to take advantage of this sensing modality to predict approaching vehicles behind the blind corner in an urban scenario. While this approach shows a lot of promise, there is a difficulty in integrating the multi-microphone system. Additionally, the system would be unable to differentiate between the nature of two sound sources. This motivates the exploration of a classification based approach which uses audio data from only a single microphone to identify the sound sources present in them. This thesis investigates the possibility of having such a system on the Intelligent Vehicle to predict approaching vehicles from behind the blind corners. A review of the literature revealed that techniques categorized under Sound Event Detection (SED) are suitable to implement a classification based approach. The prediction of the vehicle is treated as a binary classification problem and a Convolutional Recurrent Neural Network (CRNN) is used as the acoustic model to detect the presence of an approaching car in the audio sample represented by Log Mel Spectrogram features. Additionally, domain adaptation techniques were implemented to explore the possibility<br/>of improving the system performance with limited data collected while the ego-vehicle is driving. Experiments carried out indicate that when the ego-vehicle is static, the system performs well with the approaching vehicle predicted 1.4s before it is in line-of-sight and a balanced accuracy of 86.9% achieved for the classification task. However, the system achieved an accuracy of 68% on the samples recorded while the ego-vehicle was driving. Further experiments indicate that the acoustic model cannot generalize well to unseen situations in most cases and experiment with domain adaptation did not show<br/>any improvement in performance.},

note = {Kooij, J.F.P. (mentor); Hehn, T.M. (mentor); Gavrila, D. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:a88b7802-2b7c-44cb-85f6-63d0453fc9e7,

title = {Dynamic Modelling and State Estimation of a High Speed Racing Drone},

author = {Nishant Patel},

url = {http://resolver.tudelft.nl/uuid:a88b7802-2b7c-44cb-85f6-63d0453fc9e7},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Autonomous drone racing has taken a turn for the better in recent years. Drones are becoming faster and implementing better state-of-the-art control techniques to overcome different challenges. With advancements in the fields of computer vision, machine learning, and artificial intelligence, the final goal of autonomous drones is to be quicker than human-piloted racing drones. Increasing the speed of autonomous drones increases the risks associated with flying them. Time-optimal control algorithms have been identified as a method of implementing<br/>aggressive maneuvers to fly drones at high speeds throughout the course of the race. These methods require precise state-estimates. This research work identifies a model for the rate controller. The work also includes an implementation of a state estimation model with drag compensation, also merging a pre-existing refined thrust model with Coriolis effects. With the idea of developing a state estimation model for a racing drone, the model is improved to<br/>include flight envelopes involving motor saturations.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:4b92f6b0-dc40-4946-a1ae-7efd0df79401,

title = {Implicit Coordinated Tactical Avoidance for UAVs within a Geofenced Airspace},

author = {Dennis Wijngaarden},

url = {http://resolver.tudelft.nl/uuid:4b92f6b0-dc40-4946-a1ae-7efd0df79401},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {This research presents the derivation, implementation and safety assessment of a velocity obstacle- based conflict resolution method to be used by UAVs flying within a horizontally restricted airspace by a geofence under the presence of wind. Two parameters indicating the safety of the applied conflict resolution method have been measured, i.e., the Intrusion Prevention Rate (IPR) and the Violation Prevention Rate of the Geofence (VPRG). Three coordination rule-sets have been implemented i.e., 1) geometric optimum (OPT), 2) geometric optimum from target heading (DEST) and 3) only change in heading (HDG). These rule-sets have been assessed during a safety assessment. It was concluded that the OPT rule-set performed best in terms of the IPR and the DEST rule-set performed best in terms of the VPRG under windy and wind calm conditions. The HDG rule-set performed worst in terms of both safety parameters. It was noted that both safety parameters are the lowest when conflicts occur close the geofence under windy conditions for all implemented rule-sets.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:71257d1e-c65b-4eb7-9df0-869b9419a8c2,

title = {Extended Incremental Non-linear Control Allocation on the TU Delft Quadplane},

author = {Jan Karssies},

url = {http://resolver.tudelft.nl/uuid:71257d1e-c65b-4eb7-9df0-869b9419a8c2},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {This research presents an implementation of a novel controller design on an overactuated hybrid Unmanned Aerial Vehicle (UAV). This platform is a hybrid between a conventional quadcopter and a fixed-wing aircraft. Its inner loop is controlled by an existing but modified control method called Incremental Non-linear Control Allocation or INCA. This controller deals with the platform’s control allocation problem by minimising a set of objective functions with a method known as the Active Set Method and avoids actuator saturation. For the vehicle’s outer loop, a novel extension to INCA is presented, called Extended INCA or XINCA. This method optimises one of the physical actuator’s command and the angular control demands fed to the vehicle’s inner loop, based on linear reference accelerations. It does so while adapting to varying flight phases, conditions and vehicle states, and taking into account the aerodynamic properties of the main wing. XINCA has low dependence on accurate vehicle models and requires configuration using only several optimisation parameters. Both flight simulations and experimental flights are performed to prove the performance of both controllers.},

note = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:a191b5fc-4050-44b8-9652-4493d44b654c,

title = {Fuel Cell Drone for Soil Monitoring},

author = {Louis Cheung},

url = {http://resolver.tudelft.nl/uuid:a191b5fc-4050-44b8-9652-4493d44b654c},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Applied Sciences; TU Delft Electrical Engineering, Mathematics and Computer Science},

abstract = {We have set out to develop a drone, based on the existing Delftacopter, capable of soil monitoring via LiDAR remote sensing. The battery was to be replaced by a fuel cell system in order to extend the range threefold to 180km. Unfortunately, the ultimate design is likely unfeasible. Agriculture requires healthy soil and monitoring soil health is fundamental to its maintenance. Soil organic carbon in particular provides energy to the soil’s microorganisms, and is beneficial to water and nutrient retention. In addition, storing carbon in the soil is a form of carbon sequestration, which has become interesting due to the rising levels of carbon dioxide in our atmosphere. Monitoring soil organic carbon is therefore the goal of the drone design. The fuel cell system is a 650Whydrogen fuel cell by Intelligent Energy with a mass of 1290 g, which will be replacing the battery in the base design. Fuel tanks that were considered suitable are the 450 g, 0.5 L, 500 bar and 1350 g, 3 L, 300 bar fuel tanks by Meyer. It was found that one 1350 g and two 450 g fuel tanks were necessary to achieve the desired range of 180 km. However, after more careful drag estimates, this configuration turns out to be too heavy. 4 450 g fuel tanks remains feasible. Results below are based on this amount of fuel tanks. The incorporated LiDAR sensor is one by Velodyne, namely the Puck LITE, with a specified range of 100 m. The LiDAR sensor has a firing cycle of 55.296 &s, almost 20 kHz. Based on previous studies that used LiDAR to measure soil organic carbon, it has been established that a density of 5 data points per square meter is required. Fromour LiDAR parameters it turns out that the optimal flight altitude is 27.5mabove the surface that is to be measured, with a rotation rate of 10 Hz for the LiDAR sensor, when flying at a speed of 20ms¡1. With a flight distance of roughly 116km at 22.5ms¡1 (111km at 20ms¡1), an area of 21.8km2 per flight can be scanned. 1},

note = {Aravind, P.V. (mentor); Bhattacharya, Nandini (graduation committee); Remes, Bart (graduation committee); Tambi, Yash (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:256f7044-862d-4b53-b395-973dadbb7a00,

title = {Supervised Learning in Spiking Neural Networks},

author = {Bas Büller},

url = {http://resolver.tudelft.nl/uuid:256f7044-862d-4b53-b395-973dadbb7a00},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Spiking neural networks are notoriously hard to train because of their complex dynamics and sparse spiking signals. However, in part due to these properties, spiking neurons possess high computa- tional power and high theoretical energy efficiency. This thesis introduces an online, supervised, and gradient-based learning algorithm for spiking neural networks. It is shown how gradients of temporal signals that influence spiking neurons can be calculated online as an eligibility trace. The trace rep- resents the temporal gradient as a single scalar value and is recursively updated at each consecutive iteration. Moreover, the learning method uses approximate error signals to simplify their calculation and make the error calculation compatible with online learning. In several experiments, it is shown that the algorithm can solve spatial credit assignment problems with short-term temporal dependencies in deep spiking neural networks. Potential approaches for improving the algorithm’s performance on long-term temporal credit assignment problems are also discussed. Besides the research on spiking neural networks, this thesis includes an in-depth literature study on the topics of neuromorphic computing and deep learning, as well as extensive evaluations of several learning algorithms for spiking neural networks},

note = {de Croon, Guido (mentor); Paredes Valles, Federico (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:64d40dfc-d852-4688-b8f4-af37f3e9704c,

title = {How do deep neural networks perform optical flow estimation?: A neuropsychology-inspired study},

author = {David Jong},

url = {http://resolver.tudelft.nl/uuid:64d40dfc-d852-4688-b8f4-af37f3e9704c},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {End-to-end trained Convolutional Neural Networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. By obtaining an understanding of how these networks function, more can be said about the behavior of these networks in unexpected scenarios and how the architecture and training data can be improved to obtain a better performance. For our investigation, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in deep neural networks are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:88e689a1-eb19-4e48-91b2-3b122a824503,

title = {An NonlinearModel Predictive Control Approach to Autonomous UAV Racing Trajectory Generation & Control},

author = {Simon Spronk},

url = {http://resolver.tudelft.nl/uuid:88e689a1-eb19-4e48-91b2-3b122a824503},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {When observing an Autonomous Unmanned Aerial Vehicle(UAV) race, one would be hard-pressed to call it racing as the actual velocities attained are extremely low. This article addresses this shortcoming by proposing a method of generating and executing a racing trajectory for a UAV, through a series of position objectives representative of a racing environment, with the goal of significantly improving the velocity when compared to the current norm of PID controllers. The method consists of applying Nonlinear Model Predictive Control with the capability of dynamically updating the position goal based upon internal state estimation to generate a set of inputs for a UAV. To prove the viability of the proposed method we test by using numerical simulations, a flight simulator environment(Gazebo) and a series of real-world flight tests on the Bebop1 UAV. Through 2 iterations of the testing process it is proven that the method is able to significantly decrease the flight time ()through both simple and more complex short range manoeuvres(2m-4m). However model errors and an inability to fully control thrust on the UAV introduce a significant and consistent position error.},

note = {de Croon, Guido (mentor); Li, Shuo (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:fc2e13cb-4ea1-4aa7-b7f2-1d8d9478daf4,

title = {An Nonlinear Model Predictive Control Approach to Autonomous UAV Racing Trajectory Generation and Control},

author = {Simon Spronk},

url = {http://resolver.tudelft.nl/uuid:fc2e13cb-4ea1-4aa7-b7f2-1d8d9478daf4},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {hen observing an Autonomous Unmanned Aerial Vehicle(UAV) race, one would be hard-pressed to call it racing as the actual velocities attained are extremely low. This article addresses this shortcoming by proposing a method of generating and executing a racing trajectory for a UAV, through a series of position objectives representative of a racing environment, with the goal of significantly improving the velocity when compared to the current norm of PID controllers. The method consists of applying Nonlinear Model Predictive Control with the capability of dynamically updating the position goal based upon internal state estimation to generate a set of inputs for a UAV. To prove the viability of the proposed method we test by using numerical simulations, a flight simulator environment(Gazebo) and a series of real-world flight tests on the Bebop1 UAV. Through 2 iterations of the testing process it is proven that the method is able to significantly(approximately 1s) decrease the flight time through both simple and more complex short range manoeuvres(2m-4m). However model errors and an inability to fully control thrust on the UAV introduce a significant and consistent position error.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:32a90402-a577-4383-afe3-f8a865a287dc,

title = {Never Landing Drone},

author = {C. P. L. Jong},

url = {http://resolver.tudelft.nl/uuid:32a90402-a577-4383-afe3-f8a865a287dc},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Increasing endurance is a major challenge for battery-powered aerial vehicles. A method is presented which makes use of an updraft around obstacles to decrease the power consumption of a fixed-wing, unmanned aerial vehicle. Simulatory results have shown the conditions that the flight controller can fly in.<br/>The effect of a change in wind velocity, wind direction and updraft has been analysed. The simulations showed that an increase in either updraft or absolute wind direction decrease the throttle consumption.<br/>A change in wind velocity results in a shift of the flight controller’s boundaries. The simulations achieved sustained flight at 0 per cent throttle. The practical, autonomous tests reduced the average throttle down to 4.5 per cent in front of the boat. The unfavourable wind conditions and inaccuracies explain this minor<br/>throttle requirement during the final experiment.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:48040e88-f507-4676-a5da-2b701a07f387,

title = {Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs},

author = {Jesse Hagenaars},

url = {http://resolver.tudelft.nl/uuid:48040e88-f507-4676-a5da-2b701a07f387},

year  = {2020},

date = {2020-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Flying insects are capable of autonomous vision-based navigation in cluttered environments, reliably avoiding objects through fast and agile manoeuvres. Meanwhile, insect-scale micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a fraction of the energy efficiency. In light of this, it is in our interest to try and mimic flying insects in terms of their vision-based navigation capabilities, and consequently apply gained knowledge to a manoeuvre of relevance. This thesis does so through evolving spiking neural networks for controlling divergence-based landings of micro air vehicles, while minimising the network's spike rate. We demonstrate vision-based neuromorphic control for a real-world, continuous problem, as well as the feasibility of extending this controller to one that is end-to-end-learnt, and can work with an event-based camera. Furthermore, we provide insight into the resources required for successfully solving the problem of divergence-based landing, showing that high-resolution control can be learnt with only a single spiking neuron. Finally, we look at evolving only a subset of the spiking neural network's available hyperparameters, suggesting that the best results are obtained when all parameters are affected by the learning process.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{Hagenaars2020,

title = {Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs},

author = {Jesse Hagenaars},

url = {http://resolver.tudelft.nl/uuid:48040e88-f507-4676-a5da-2b701a07f387},

year  = {2020},

date = {2020-01-01},

address = {Delft, NL},

school = {Delft University of Technology},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}