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: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: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}

}

@mastersthesis{uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2,

title = {Motion-based MAV Detection in GPS-denied Environments},

author = {Erik Vroon},

url = {http://resolver.tudelft.nl/uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:9b68db7c-ac32-422e-8749-a8e0bc1fc4ca,

title = {Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes},

author = {Benjamin Keltjens},

url = {http://resolver.tudelft.nl/uuid:9b68db7c-ac32-422e-8749-a8e0bc1fc4ca},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. Although these methods show strong results on outdoor datasets such as KITTI, they do not match performance of supervised methods on indoor environments with camera rotation. Indoor, rotated scenes are common for less constrained applications and pose problems for two reasons: abundance of low texture regions and increased complexity of depth cues for images under rotation. In an effort to extend self-supervised learning to more generalised environments we propose two additions. First, we propose a novel Filled Disparity Loss term that corrects for ambiguity of image reconstruction error loss in textureless regions. Specifically, we interpolate disparity in untextured regions, using the estimated disparity from surrounding textured areas, and use L1 loss to correct the original estimation. Our experiments show that depth estimation is substantially improved on low-texture scenes, without any loss on textured scenes, when compared to Monodepth by Godard et al. Secondly, we show that training with an application's representative rotations, in both pitch and roll, is sufficient to significantly improve performance over the entire range of expected rotation. We demonstrate that depth estimation is successfully generalised as performance is not lost when evaluated on test sets with no camera rotation. Together these developments enable a broader use of self-supervised learning of monocular depth estimation for complex environments.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:5aa0ed78-c775-4a1b-a0ab-5145f85e5e9d,

title = {Feasibility of using electric drone main rotors for electricity generation vs. solar panels for indefinite flight},

author = {Nicholas Dvorsky},

url = {http://resolver.tudelft.nl/uuid:5aa0ed78-c775-4a1b-a0ab-5145f85e5e9d},

year  = {2021},

date = {2021-01-01},

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

abstract = {An idea was proposed to allow an autonomous drone to have indefinite flight times over the ocean by applying renewable energy technologies and theory to generate electricity in flight. This is considered less as a way to save energy, but to permit the use of such a drone from a ship not capable of safely retrieving it. One novel component of this idea is to use the wind updraft created by the motion of a ship or natural air currents as the wind source for an on-board turbine generator. The second component is to use the existing drive system as the on-board turbine in a 'hybrid rotor' design to reduce the need for extra parts and complexity. This report analyzes the potential for such a system compared to a more intuitive airborne solar system, and to the combination of both concepts. While indefinite flight time is paramount, the goal is to maximize the "mission" time to charge/idle time ratio. The process for determining fitness is a simulation of the aircraft flying on its mission and charging when needed (and if possible) for a full year for varying designs of aircraft and rotor. The results of all the tests show that the main idea is infeasible because not enough energy can be generated from the inefficient propeller and the updrafts are insufficient and inconsistent. The alternatives of solar and combined power systems function better but are still subject to high failure rates. The most promising system is to use a separate turbine and propeller and also include solar panels to achieve the most effectiveness both when in powered flight and while charging. This constitutes a compromise on the 'hybrid rotor' part of the idea. The conclusion of this report is that further improvements to the design and control of the most successful configuration are possible could result in a fully functional system.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:f46c66f3-60e5-4285-9cf3-978826387526,

title = {Attitude Control of Flapping-Wing Air Vehicles},

author = {Bas Roulaux},

url = {http://resolver.tudelft.nl/uuid:f46c66f3-60e5-4285-9cf3-978826387526},

year  = {2021},

date = {2021-01-01},

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

abstract = {Flapping-Wing Air Vehicles (FWAV) are autonomously flying vehicles that use their flapping wings to simultaneously stay aloft and enable controllable flight. FWAVs that are capable of controllable flight are reported in literature, though a theoretical background of the aerodynamic performance of different attitude control mechanisms is absent in literature and the robustness of attitude control mechanisms with respect to body motions is oftentimes omitted. The aim of this thesis is to develop a theoretical framework for the aerodynamic response of flapping wings that includes variation of attitude control parameters and motion of the vehicle body. This framework can be used to assist in research into new attitude control mechanisms for FWAVs that are not yet capable of attitude control, such as the compliant Atalanta FWAV. Analytical aerodynamic and kinematic descriptions are combined to analyze the aerodynamic performance of two suggested attitude control mechanisms: stroke amplitude variations and control of the angle of attack by means of pitching stiffness variations. It is shown in this research that both mechanisms have a significant influence on the lift production of a flapping wing, though this influence changes significantly when body motions are introduced. It is found that variations of the stroke amplitude provide the most predictable variations in lift for all cases of body motion that were considered, provided that the wing’s pitching hinge stiffness is high enough to ensure stable flapping kinematics under the influence of body motion.},

note = {Goosen, J.F.L. (mentor); Remes, B.D.W. (graduation committee); van der Wijk, V. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:019566ca-34de-4ddd-87ac-86364ef2759b,

title = {Obstacle Avoidance onboard MAVs using a FMCW RADAR},

author = {Nikhil Wessendorp},

url = {http://resolver.tudelft.nl/uuid:019566ca-34de-4ddd-87ac-86364ef2759b},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Micro air vehicles (MAVs) are increasingly being considered for aerial tasks such as delivery of goods and surveillance due to their lightweight, compact design and manoeuvrability. To safely and reliably carry out these tasks and navigate to its objective, especially in complex and cluttered environments, the MAV is also required to sense and avoid (S&A) obstacles. Due to the MAVs limitations in weight, power and processing power, vision systems usually prove ideal for sensing the environment, being a cheap, lightweight, power efficient and a rich source of information. They do however require adequate computational resources and most importantly, good visibility. When the environment does not host these conditions, for instance when flying though dust, smoke or fog, other sensors need to be utilised that can provide more robust sensing to ensure safe and reliable operation. Radar sensors are mostly unaffected by atmospheric conditions and have been used extensively in the aerospace industry for this purpose. These sensors were traditionally heavy and power hungry, only applicable on ground or in large craft. However other radar sensors have since come about that are more suited for use in small MAVs. Specifically, lightweight, power efficient and compact frequency modulated continuous wave (FMCW) radars have increasingly been used in advanced driver assistance systems as auxiliary sensors, however there has been little work to integrate them on MAVs. This sensor provides the range, horizontal bearing and radial velocity (Doppler shift) of any objects in the field of view, which can then be used for multi-target tracking (MTT) [38]. The major disadvantage of the sensor is the limited field of view (approximately 80 degrees horizontal) and noisy nature of the sensor, especially in cluttered environments. The challenge is to explore filtering, tracking and avoidance algorithm pipelines to extract meaningful information from the raw data and investigate the sensor’s effectiveness with respect to obstacle avoidance on MAVs. This will include algorithms such as data association, estimation and avoidance, as well as an investigation of neural networks to aid in processing the raw data and provide some filtering. This will be accomplished by integrating the sensor on a MAV and testing and tuning the algorithms both in real life (in the cyberzoo flying arena of the aerospace faculty), and using data gathered as part of an obstacle detection and avoidance dataset that was generated during this project. This will hopefully allow MAVs to operate safer, either using a standalone radar or integrated with other sensors.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:a4497602-a257-4561-8e4c-5baa36e8cd6f,

title = {Urban MAV: A visual odometry dataset},

author = {Quincy Booster},

url = {http://resolver.tudelft.nl/uuid:a4497602-a257-4561-8e4c-5baa36e8cd6f},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {The most common used classification of drones is Micro air vehicles (MAV), with quadrotors being the most conventional MAV [1]. Its relatively small size and rotary wings provide high maneuverability, including vertical takeoff and hovering, making them useful in confined and hard to reach spaces. Another benefit of the MAV in comparison to other drone classifications is its relatively smaller production costs [1]. Because of these features they play an increasing role within our society by aiding in human tasks, by applying them in for example site inspection, agriculture and rescue missions [2–4]. However, its agility comes at a cost which takes the form of high power consumption [5]. Making improving MAV designs a challenge.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:8f90894b-933a-4649-90a0-1bbc1de6c0d6,

title = {High-Dimensional Optimal State-Feedback Mapping using Deep Neural Networks for Agile Quadrotor Flight},

author = {Rohan Chotalal},

url = {http://resolver.tudelft.nl/uuid:8f90894b-933a-4649-90a0-1bbc1de6c0d6},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {For most robotics applications, optimal control remains a promising solution for solving complex control tasks. One example is the time-optimal flight of Micro Air Vehicles (MAVs), where strict computational requirements fail to resolve such algorithms onboard. Recent work on the use of deep neural networks for guidance and control (G&CNets) has shown that these biologically inspired models approximate well the optimal control solution while requiring a fraction of the computational cost. Although previous attempts resulted in successful flight tests, training occurred on large-scale datasets based on a 3-DoF model. Since model refinement leads to higher generation time, in this work, we show that G&CNets trained on small-sized datasets can mimic the optimal control solution of a full 6-DoF quadrotor model. The cost function used in the generation process penalizes the altitude error and mixes both time and power-optimal objectives weighted by a varying homotopy parameter. Trained networks output the vertical thrust command and body rates based on the vehicle's position, velocity, and attitude. The proposed controller transfers well onboard for different flight scenarios: (i) longitudinal, lateral and diagonal flight; (ii) hovering with and without the effect of disturbances and (iii) waypoint tracking experiment. Through a Monte-Carlo test campaign, it is demonstrated that G&CNets trained on small datasets provide similar results to those with 100 times more samples. To the best of our knowledge, this work is the first implementation of a high-dimensional G&CNet in the control loop of a real MAV.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:85b358c9-8018-4e0c-867e-f35fe26716cb,

title = {Obstacle Detection and Avoidance onboard an MAV using a Monocular Event-based Camera},

author = {Raoul Dinaux},

url = {http://resolver.tudelft.nl/uuid:85b358c9-8018-4e0c-867e-f35fe26716cb},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Micro Air Vehicles (MAVs) are able to support humans in dangerous operations, such as search and rescue operations at night on unknown terrain. These scenes require a great amount of autonomy from the MAV, as they are often radio and GPS-denied. As MAVs have limited computational resources and energy storage, onboard navigation tasks have to be performed efficient and fast. To address this challenge, this research proposes an approach to visual obstacle detection and avoidance onboard an MAV. The algorithmic approach is based on event-based optic flow, using a monocular event-based camera. This camera captures the apparent motion in the scene, has microsecond latency and very low power consumption, therefore a good fit for onboard navigation tasks. Firstly, a literature study is performed to provide theoretical concepts and foundation for the obstacle avoidance approach. A processing pipeline is designed, based on the use of event-based normal optic flow. This pipeline consists of three sections: course estimation, obstacle detection and obstacle avoidance. A novel course estimation method 'FAITH' is proposed which uses optic flow half-planes along with a fast RANSAC scheme. The object detection method is based on DBSCAN clustering of optic flow vectors, using the time-to-contact and vector location as clustering variables. The performance of these methods is experimentally demonstrated by three experiments: in a simulated environment, offline on real sensor data and online onboard an MAV. As currently no event-based obstacle avoidance datasets are publicly available, a dataset is recorded as supplement to this and future research. Approximately 1350 runs of event-based camera, RADAR, IMU and OptiTrack data are recorded, manually avoiding either a single or two poles using an MAV in the flying arena of the TU Delft. This dataset is used in this research to determine the performance of the course estimation method using real sensor data. The course estimation method is shown to have state-of-the-art accuracy and beyond state-of-the-art computation time on both simulated data and the recorded dataset. The final experiment shows the obstacle detection and avoidance approach integrated onboard an MAV in a real-time obstacle avoidance task. The approach is shown to have a success rate of 80% in a frontal obstacle avoidance task on a low-textured 50-cm wide pole. The contribution of this research is an obstacle detection and avoidance approach using a monocular event-based camera onboard an MAV, along with the novel course estimation algorithm 'FAITH'.},

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:665779ce-5080-43da-8519-4cd17e2f105d,

title = {Time-Optimal Control for Tiny Quadcopters},

author = {Jelle Westenberger},

url = {http://resolver.tudelft.nl/uuid:665779ce-5080-43da-8519-4cd17e2f105d},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Time-optimal model-predictive control is essential in achieving fast and adaptive quadcopter flight. Due to the limited computational performance of onboard hardware, aggressive flight approaches have relied on off-line trajectory optimization processes or non time-optimal methods. In this work we propose a computational efficient model predictive controller (MPC) that approaches time-optimal flight and runs onboard a consumer quadcopter. The proposed controller is built on the principle that constrained optimal control problems (OCPs) have a so-called 'bang-bang' solution. Our solution plans a bang-bang maneuver in the critical direction while aiming for a 'minimum-effort' approach in non-critical direction. Control parameters are computed by means of a bisection scheme using an analytical path prediction model. The controller has been compared with a classical PID controller and theoretical time-optimal trajectories in simulations. We identify the consequences of the OCP simplifications and propose a method to mitigate one of these effects. Finally, we have implemented the proposed controller onboard a consumer quadcopter and performed indoor flights to compare the controller's performance to a PID controller. Flight experiments have shown that the controller runs at 512hz onboard a Parrot Bebop quadcopter and is capable of fast, saturated flight, outperforming traditional PID controllers in waypoint-to-waypoint flight while requiring only minimal knowledge of the quadcopter's dynamics.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:9f478516-7129-4e6f-9b84-d66fad419d51,

title = {Acoustic-Based Aircraft Detection and Ego-Noise Suppression: for Micro Aerial Vehicles},

author = {Mark Woude},

url = {http://resolver.tudelft.nl/uuid:9f478516-7129-4e6f-9b84-d66fad419d51},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Widespread usage of Micro Aerial Vehicles (MAVs) has led to various airspace safety breaches, including near mid-air collisions with other aircraft. To ensure safe integration into general aviation, it is paramount that MAVs are equipped with an autonomous detect and avoid system when flying beyond the visual line-of-sight of the operator. The purpose of this research is to investigate the feasibility of acoustic-based aircraft detection, which has generally been overlooked in favor of optical or radar-based technology. Effective sound-based aircraft detection on-board an MAV requires suppressing the dynamic ego-noise it generates during flight, which would otherwise pollute the recorded environmental sound. This paper proposes using a recurrent neural network to predict the generated noise, given a sequence of MAV flight data, so that it can be effectively removed from noisy recordings. For aircraft detection, a convolutional neural network in combination with Mel spectrogram features is designed to classify noise-free environmental sound as either aircraft or non-aircraft, achieving 97.5% accuracy. To reconstruct the noisy environment of an MAV flight, these noise-free sounds are mixed with ego-noise at mix ratios up to 1.00. When evaluating in these mismatched conditions, accuracy decreases to 95.0% and 47.5% with- and without ego-noise suppression, respectively. Although ego-noise suppression can not prevent a drop in performance, the large difference between the mismatched conditions does demonstrate the benefits of the proposed denoising approach on aircraft detection.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:022cdfe2-737e-44f4-8497-0159a826b9ea,

title = {Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control},

author = {Diego De Buysscher},

url = {http://resolver.tudelft.nl/uuid:022cdfe2-737e-44f4-8497-0159a826b9ea},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to promote learning efficiency. On the other, safe learning provides a framework to consider a system’s safety during the learning process. The latter’s contribution is significant on safety-critical systems, such as transport vehicles where stringent (safety) requirements apply. This pa- per proposes a black box safe curriculum learning architecture applicable to systems with unknown dynamics. It only requires knowledge of the state and action spaces’ orders for a given task and system. By adding system identification capabilities to existing safe cur- riculum learning paradigms, the proposed architecture successfully ensures safe learning proceedings of tracking tasks without requiring initial knowledge of internal system dynam- ics. More specifically, a model estimate is generated online to complement safety filters that rely on uncertain models for their safety guarantees. This research explicitly targets linearised systems with decoupled dynamics in the experiments provided in this article as proof of concept. The paradigm is initially verified on a mass-spring-damper system. After that, the architecture is applied to a quadrotor where it is able to successfully track the system’s four degrees of freedom independently, namely attitude angles and altitude. The RL agent is able to safely learn an optimal policy that can track an independent reference on each degree of freedom.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:1eed4184-5c4a-4e88-b54d-8c9751f79ebf,

title = {Fleet Planning Under Demand and Fuel Price Uncertainty Using Actor-Critic Reinforcement Learning},

author = {Izaak Geursen},

url = {http://resolver.tudelft.nl/uuid:1eed4184-5c4a-4e88-b54d-8c9751f79ebf},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Current state-of-the-art airline planning models are required to decrease models either in size or complexity due to computational limitations, limiting the 
operational applicability to problems of representative sizes. Models return suboptimal solutions, especially when confronted with factors of uncertainty. Considering the growing interest in the application of Machine Learning techniques in the Operations Research domain, and the proven success in other fields such as robotics, this research investigates the applicability of these techniques for airline planning. An Advantage Actor-Critic (A2C) Reinforcement Learning agent is applied to the airline fleet planning problem. Because of the increased computational efficiency of using an A2C agent, the problem is increased in size and the highly volatile uncertainty in fuel price is implemented.
Conversion was achieved, and when evaluating the quality of the solutions compared to a deterministic model, the performance was very satisfactory. The A2C agent was able to outperform the deterministic model, with an increasing performance as more complexity was added to the problem. It was found that
the introduction of additional uncertainty has a major effect on the optimal actions, which the agent was able to adapt to adequately.},

note = {Lopes Dos Santos, Bruno (mentor); Yorke-Smith, Neil (graduation committee); de Croon, Guido (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:e44e5449-519b-4a21-81e3-a96f1bbb2811,

title = {Sample-efficient multi-agent reinforcement learning using learned world models},

author = {Daniël Willemsen},

url = {http://resolver.tudelft.nl/uuid:e44e5449-519b-4a21-81e3-a96f1bbb2811},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {Multi-agent robotic systems could benefit from reinforcement learning algorithms that are able to learn behaviours in a small number trials, a property known as sample efficiency. This research investigates the use of learned world models to create more sample-efficient algorithms. We present a novel multi-agent model-based reinforcement learning algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing the Centralized Learning for Decentralized Execution (CLDE) framework, and demonstrate state-of-the-art performance in terms of sample efficiency on a number of benchmark domains. CLDE algorithms allow a group of agents to act in a fully decentralized manner after training. This is a desirable property for many systems comprising of multiple robots. Current CLDE algorithms such as Multi-Agent Soft Actor-Critic (MASAC) suffer from limited sample efficiency, often taking many thousands of trials before learning desirable behaviours. This makes these algorithms impractical for learning in real-world robotic tasks. MAMBPO utilizes a learned world model to improve sample efficiency compared to its model-free counterparts. We demonstrate on two simulated multi-agent robotics tasks that MAMBPO is able to reach similar performance to MASAC with up to 3.7 times fewer samples required for learning. Doing this, we take an important step towards making real-life learning for multi-agent robotic systems possible.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:35ad1e1c-a3c3-456a-a899-06dac1dc3398,

title = {Improving the Performance of INDI Flight Control for a Quadrotor in the Ceiling Effect},

author = {Max Kemmeren},

url = {http://resolver.tudelft.nl/uuid:35ad1e1c-a3c3-456a-a899-06dac1dc3398},

year  = {2021},

date = {2021-01-01},

school = {TU Delft Aerospace Engineering},

abstract = {As the application areas of Unmanned Aerial Vehicles (UAVs) keep expanding, new flight areas are encountered more often. Small UAVs, named Micro Air Vehicles (MAVs), even fly in areas like sewage pipes. These areas introduce new difficulties such as aerodynamic effects caused by the ground and/or ceiling. In this paper two main contributions are presented that deal with the aerodynamic effects caused by the ceiling: 1) an adaptive model describing the ceiling effect using onboard measurements, which can be altered to describe other aerodynamic effects that occur when flying in constrained spaces, 2) incorporating the adaptive model into an Incremental Nonlinear Dynamic Inversion (INDI) controller. The controller is implemented and tested onto a MAV (Crazyflie). The results have shown stability improvements for close ceiling flight. Moreover the minimal distance the MAV can fly from the ceiling is decreased using the new controller.},

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@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}

}