Publications

@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}

}