Publications

@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<br/>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<br/>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<br/>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.<br/>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. (mentor); 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. <br/>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. (graduation committee); Wang, S. (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<br/>these days, such as short-range inspections<br/>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.<br/><br/>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.<br/><br/>This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight<br/>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:<br/>a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.<br/>In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies<br/>are of minimal influence. However, the angular rate damper<br/>strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.<br},

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:83ff9b16-d3bb-4ac7-aa8a-26f4a73a6e6d,

title = {DroneCrane},

author = {Aaron Bracke},

url = {http://resolver.tudelft.nl/uuid:83ff9b16-d3bb-4ac7-aa8a-26f4a73a6e6d},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering; Džubinský, Maxim},

abstract = {Drones have been an emerging trend in the last few years. They are used in multiple industries<br/>already, from photography and videography to racing. More use cases are now being conceived,<br/>such as using drones to deliver packages and food to people at home, using drones for inspections,<br/>or even using them as rescue searching vehicles in hostile environments. Even more possibilities<br/>open up once the drones bundle their forces to create swarms. The lifting capabilities of drones are<br/>still somewhat limited, but in a swarm they might be able to lift heavy payloads. This report covers<br/>the design of a concept of a payload carrying swarm, intended to lift cargo up to 500 kg to even the<br/>top of a tall building.},

note = {Smeur, E.J.J. (mentor); Eleftheroglou, N. (graduation committee); van 't Hoff, J.A. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

@mastersthesis{uuid:5184c93c-fafb-4069-a6b0-75acdb42f081,

title = {Aerial Deployment of an Autonomous Remote Sensing Network},

author = {Marco Xausa},

url = {http://resolver.tudelft.nl/uuid:5184c93c-fafb-4069-a6b0-75acdb42f081},

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering; van der Saag, Jelmer},

note = {Dransfeld, C.A. (mentor); Hamaza, S. (mentor); Hwang, S. (mentor); Gomes de Paula, N.C. (mentor); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

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

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

author = {Roelof Stikker},

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

year  = {2022},

date = {2022-01-01},

school = {TU Delft Aerospace Engineering},

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

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

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}