2021
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Masters Theses
|
Benjamin Keltjens Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes (Masters Thesis) TU Delft Aerospace Engineering, 2021, (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)). @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}
}
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. |
Nicholas Dvorsky Feasibility of using electric drone main rotors for electricity generation vs. solar panels for indefinite flight (Masters Thesis) TU Delft Electrical Engineering, Mathematics and Computer Science, 2021, (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)). @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}
}
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. |
Bas Roulaux Attitude Control of Flapping-Wing Air Vehicles (Masters Thesis) TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Precision and Microsystems Engineering, 2021, (Goosen, J.F.L. (mentor); Remes, B.D.W. (graduation committee); van der Wijk, V. (graduation committee); Delft University of Technology (degree granting institution)). @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}
}
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. |
Nikhil Wessendorp Obstacle Avoidance onboard MAVs using a FMCW RADAR (Masters Thesis) TU Delft Aerospace Engineering, 2021, (Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (mentor); Fioranelli, F. (graduation committee); Delft University of Technology (degree granting institution)). @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}
}
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. |
Quincy Booster Urban MAV: A visual odometry dataset (Masters Thesis) TU Delft Aerospace Engineering, 2021, (de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @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}
}
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. |
Rohan Chotalal High-Dimensional Optimal State-Feedback Mapping using Deep Neural Networks for Agile Quadrotor Flight (Masters Thesis) TU Delft Aerospace Engineering, 2021, (de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @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}
}
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. |
Raoul Dinaux Obstacle Detection and Avoidance onboard an MAV using a Monocular Event-based Camera (Masters Thesis) TU Delft Aerospace Engineering, 2021, (de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)). @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}
}
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'. |
Jelle Westenberger Time-Optimal Control for Tiny Quadcopters (Masters Thesis) TU Delft Aerospace Engineering, 2021, (de Croon, G.C.H.E. (mentor); de Wagter, C. (graduation committee); Delft University of Technology (degree granting institution)). @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}
}
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. |
Mark Woude Acoustic-Based Aircraft Detection and Ego-Noise Suppression: for Micro Aerial Vehicles (Masters Thesis) TU Delft Aerospace Engineering, 2021, (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)). @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}
}
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. |
Diego De Buysscher Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control (Masters Thesis) TU Delft Aerospace Engineering, 2021, (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)). @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}
}
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. |
Izaak Geursen Fleet Planning Under Demand and Fuel Price Uncertainty Using Actor-Critic Reinforcement Learning (Masters Thesis) TU Delft Aerospace Engineering, 2021, (Lopes Dos Santos, Bruno (mentor); Yorke-Smith, Neil (graduation committee); de Croon, Guido (graduation committee); Delft University of Technology (degree granting institution)). @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 <br/>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.<br/>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<br/>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}
}
Current state-of-the-art airline planning models are required to decrease models either in size or complexity due to computational limitations, limiting the <br/>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.<br/>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<br/>the introduction of additional uncertainty has a major effect on the optimal actions, which the agent was able to adapt to adequately. |
Daniël Willemsen Sample-efficient multi-agent reinforcement learning using learned world models (Masters Thesis) TU Delft Aerospace Engineering, 2021, (Coppola, M. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @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}
}
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. |
Max Kemmeren Improving the Performance of INDI Flight Control for a Quadrotor in the Ceiling Effect (Masters Thesis) TU Delft Aerospace Engineering, 2021, (Smeur, E.J.J. (mentor); de Wagter, C. (mentor); Delft University of Technology (degree granting institution)). @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}
}
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. |
Miscellaneous
|
Felix Stache; Jonas Westheider; Federico Magistri; Marija Popović; Cyrill Stachniss Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation (Miscellaneous) 2021. @misc{2108.01884,
title = {Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation},
author = {Felix Stache and Jonas Westheider and Federico Magistri and Marija Popović and Cyrill Stachniss},
url = {https://arxiv.org/abs/2108.01884},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Liren Jin; Julius Rückin; Stefan H. Kiss; Teresa Vidal-Calleja; Marija Popović Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels (Miscellaneous) 2021. @misc{2109.14257,
title = {Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels},
author = {Liren Jin and Julius Rückin and Stefan H. Kiss and Teresa Vidal-Calleja and Marija Popović},
url = {https://arxiv.org/abs/2109.14257},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Julius Rückin; Liren Jin; Marija Popović Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing (Miscellaneous) 2021. @misc{2109.13570,
title = {Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing},
author = {Julius Rückin and Liren Jin and Marija Popović},
url = {https://arxiv.org/abs/2109.13570},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Benjamin Keltjens; Tom Dijk; Guido Croon Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes (Miscellaneous) 2021. @misc{2106.12958,
title = {Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes},
author = {Benjamin Keltjens and Tom Dijk and Guido Croon},
url = {https://arxiv.org/abs/2106.12958},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Jesse Hagenaars; Federico Paredes-Vallés; Guido Croon Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks (Miscellaneous) 2021. @misc{2106.01862,
title = {Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks},
author = {Jesse Hagenaars and Federico Paredes-Vallés and Guido Croon},
url = {https://arxiv.org/abs/2106.01862},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Shushuai Li; Christophe De Wagter; Guido C. H. E. Croon Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks (Miscellaneous) 2021. @misc{2105.12797,
title = {Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks},
author = {Shushuai Li and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2105.12797},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Mario Coppola; Jian Guo; Eberhard Gill; Guido C. H. E. Croon A model-based framework for learning transparent swarm behaviors (Miscellaneous) 2021. @misc{2103.05343,
title = {A model-based framework for learning transparent swarm behaviors},
author = {Mario Coppola and Jian Guo and Eberhard Gill and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2103.05343},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Daniël Willemsen; Mario Coppola; Guido C. H. E. Croon MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models (Miscellaneous) 2021. @misc{2103.03662,
title = {MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models},
author = {Daniël Willemsen and Mario Coppola and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2103.03662},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Nikhil Wessendorp; Raoul Dinaux; Julien Dupeyroux; Guido Croon Obstacle Avoidance onboard MAVs using a FMCW RADAR (Miscellaneous) 2021. @misc{2103.02050,
title = {Obstacle Avoidance onboard MAVs using a FMCW RADAR},
author = {Nikhil Wessendorp and Raoul Dinaux and Julien Dupeyroux and Guido Croon},
url = {https://arxiv.org/abs/2103.02050},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Julien Dupeyroux; Stein Stroobants; Guido Croon A toolbox for neuromorphic sensing in robotics (Miscellaneous) 2021. @misc{2103.02751,
title = {A toolbox for neuromorphic sensing in robotics},
author = {Julien Dupeyroux and Stein Stroobants and Guido Croon},
url = {https://arxiv.org/abs/2103.02751},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
M. Karasek; Rick Ruisink Attitude control mechanism for a flapping wing aerial vehicle (Miscellaneous) 2021, (B64C). @misc{3f4358a1911348b4b977cc0f32820c60,
title = {Attitude control mechanism for a flapping wing aerial vehicle},
author = {M. Karasek and Rick Ruisink},
url = {https://research.tudelft.nl/en/publications/attitude-control-mechanism-for-a-flapping-wing-aerial-vehicle},
year = {2021},
date = {2021-01-01},
note = {B64C},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Marina González-Álvarez; Julien Dupeyroux; Federico Corradi; Guido Croon Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp (Miscellaneous) 2021. @misc{2110.00646,
title = {Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp},
author = {Marina González-Álvarez and Julien Dupeyroux and Federico Corradi and Guido Croon},
url = {https://arxiv.org/abs/2110.00646},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Stein Stroobants; Julien Dupeyroux; Guido Croon Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors (Miscellaneous) 2021. @misc{2109.10199,
title = {Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors},
author = {Stein Stroobants and Julien Dupeyroux and Guido Croon},
url = {https://arxiv.org/abs/2109.10199},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Bardienus P. Duisterhof; Shushuai Li; Javier Burgués; Vijay Janapa Reddi; Guido C. H. E. Croon Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments (Miscellaneous) 2021. @misc{2107.05490,
title = {Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments},
author = {Bardienus P. Duisterhof and Shushuai Li and Javier Burgués and Vijay Janapa Reddi and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2107.05490},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Nitin J. Sanket; Chahat Deep Singh; Chethan M. Parameshwara; Cornelia Fermüller; Guido C. H. E. Croon; Yiannis Aloimonos EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following (Miscellaneous) 2021. @misc{2106.15045,
title = {EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following},
author = {Nitin J. Sanket and Chahat Deep Singh and Chethan M. Parameshwara and Cornelia Fermüller and Guido C. H. E. Croon and Yiannis Aloimonos},
url = {https://arxiv.org/abs/2106.15045},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sven Pfeiffer; Christophe Wagter; Guido Croon Data underlying the publication: "A Computationally Efficient Moving Horizon Estimator for UWB Localization on Small Quadrotors"" (Miscellaneous) 2021. @misc{Pfeiffer2021,
title = {Data underlying the publication: "A Computationally Efficient Moving Horizon Estimator for UWB Localization on Small Quadrotors""},
author = {Sven Pfeiffer and Christophe Wagter and Guido Croon},
url = {https://data.4tu.nl/articles/dataset/Data_underlying_the_publication_A_Computationally_Efficient_Moving_Horizon_Estimator_for_UWB_Localization_on_Small_Quadrotors_/14827680},
doi = {10.4121/14827680.v1},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Julien Dupeyroux; Nikhil Wessendorp; Raoul Dinaux; Guido De Croon The Obstacle Detection and Avoidance Dataset for Drones (Miscellaneous) 2021. @misc{https://doi.org/10.4121/14214236.v1,
title = {The Obstacle Detection and Avoidance Dataset for Drones},
author = {Julien Dupeyroux and Nikhil Wessendorp and Raoul Dinaux and Guido De Croon},
url = {https://data.4tu.nl/articles/dataset/The_Obstacle_Detection_and_Avoidance_Dataset_for_Drones/14214236/1},
doi = {10.4121/14214236.v1},
year = {2021},
date = {2021-01-01},
publisher = {4TU.ResearchData},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
PhD Theses
|
M. Coppola Automatic Design of Verifiable Robot Swarms (PhD Thesis) Delft University of Technology, 2021, ISBN: 978-94-6421-287-7. @phdthesis{b6ad7dddc6604aab827765f7a22a4a52,
title = {Automatic Design of Verifiable Robot Swarms},
author = {M. Coppola},
url = {https://research.tudelft.nl/en/publications/automatic-design-of-verifiable-robot-swarms},
doi = {10.4233/uuid:b6ad7ddd-c660-4aab-8277-65f7a22a4a52},
isbn = {978-94-6421-287-7},
year = {2021},
date = {2021-01-01},
school = {Delft University of Technology},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
|
S. Li Autonomous Swarms of Tiny Flying Robots (PhD Thesis) Delft University of Technology, 2021, ISBN: 978-94-6366-472-1. @phdthesis{825f3a6b30394a6e8f3f9f0871bd9ce5,
title = {Autonomous Swarms of Tiny Flying Robots},
author = {S. Li},
url = {https://research.tudelft.nl/en/publications/autonomous-swarms-of-tiny-flying-robots},
doi = {10.4233/uuid:825f3a6b-3039-4a6e-8f3f-9f0871bd9ce5},
isbn = {978-94-6366-472-1},
year = {2021},
date = {2021-01-01},
school = {Delft University of Technology},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
|
2020
|
Journal Articles
|
Shuo Li; Michaël M. O. I. Ozo; Christophe De Wagter; Guido C. H. E. Croon Autonomous drone race: A computationally efficient vision-based navigation and control strategy (Journal Article) In: Robotics and Autonomous Systems, vol. 133, 2020, ISSN: 0921-8890. @article{022f5ed1ba26449f8851254541a93267,
title = {Autonomous drone race: A computationally efficient vision-based navigation and control strategy},
author = {Shuo Li and Michaël M. O. I. Ozo and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/autonomous-drone-race-a-computationally-efficient-vision-based-na},
doi = {10.1016/j.robot.2020.103621},
issn = {0921-8890},
year = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
journal = {Robotics and Autonomous Systems},
volume = {133},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Shuo Li; Micha"el M. O. I. Ozo; Christophe De Wagter; Guido C. H. E. Croon Autonomous drone race: A computationally efficient vision-based navigation and control strategy (Journal Article) In: Robotics and Autonomous Systems, vol. 133, 2020, ISSN: 0921-8890. @article{022f5ed1ba26449f8851254541a93267b,
title = {Autonomous drone race: A computationally efficient vision-based navigation and control strategy},
author = {Shuo Li and Micha"el M. O. I. Ozo and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/autonomous-drone-race-a-computationally-efficient-vision-based-na},
doi = {10.1016/j.robot.2020.103621},
issn = {0921-8890},
year = {2020},
date = {2020-11-01},
journal = {Robotics and Autonomous Systems},
volume = {133},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
J.J. Hagenaars, F. Paredes-Vallés, S.M. Bohté, G.C.H.E. de Croon Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs (Journal Article) In: IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6239 - 6246, 2020. @article{jesse_neuroevolution_divLanding,
title = {Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs},
author = {J.J. Hagenaars, F. Paredes-Vallés, S.M. Bohté, G.C.H.E. de Croon},
url = {https://ieeexplore.ieee.org/abstract/document/9149674/metrics#metrics},
doi = {10.1109/LRA.2020.3012129},
year = {2020},
date = {2020-10-04},
journal = {IEEE Robotics and Automation Letters},
volume = {5},
number = {4},
pages = { 6239 - 6246},
abstract = {Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a much higher energy consumption. In light of this, we want to mimic flying insects in terms of their processing capabilities, and consequently show the efficiency of this approach in the real world. This letter does so through evolving spiking neural networks for controlling landings of micro air vehicles using optical flow divergence from a downward-looking camera. We demonstrate that the resulting neuromorphic controllers transfer robustly from a highly abstracted simulation to the real world, performing fast and safe landings while keeping network spike rate minimal. Furthermore, we provide insight into the resources required for successfully solving the problem of divergence-based landing, showing that high-resolution control can be learned with only a single spiking neuron. To the best of our knowledge, this work is the first to integrate spiking neural networks in the control loop of a real-world flying robot. Videos of the experiments can be found at https://bit.ly/neuro-controller.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Flying insects are capable of vision-based navigation in cluttered environments, reliably avoiding obstacles through fast and agile maneuvers, while being very efficient in the processing of visual stimuli. Meanwhile, autonomous micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a much higher energy consumption. In light of this, we want to mimic flying insects in terms of their processing capabilities, and consequently show the efficiency of this approach in the real world. This letter does so through evolving spiking neural networks for controlling landings of micro air vehicles using optical flow divergence from a downward-looking camera. We demonstrate that the resulting neuromorphic controllers transfer robustly from a highly abstracted simulation to the real world, performing fast and safe landings while keeping network spike rate minimal. Furthermore, we provide insight into the resources required for successfully solving the problem of divergence-based landing, showing that high-resolution control can be learned with only a single spiking neuron. To the best of our knowledge, this work is the first to integrate spiking neural networks in the control loop of a real-world flying robot. Videos of the experiments can be found at https://bit.ly/neuro-controller. |
Jesse J. Hagenaars; Federico Paredes-Vallés; Sander M. Bohté; Guido C. H. E. De Croon Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs (Journal Article) In: IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6239–6246, 2020, ISSN: 2377-3766. @article{3b2f0ad360184495a233f6dc3b57ea23,
title = {Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs},
author = {Jesse J. Hagenaars and Federico Paredes-Vallés and Sander M. Bohté and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/evolved-neuromorphic-control-for-high-speed-divergence-based-land},
doi = {10.1109/LRA.2020.3012129},
issn = {2377-3766},
year = {2020},
date = {2020-10-01},
journal = {IEEE Robotics and Automation Letters},
volume = {5},
number = {4},
pages = {6239–6246},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Guido Croon Flapping wing drones show off their skills (Journal Article) In: Science Robotics, vol. 5, no. 44, 2020, ISSN: 2470-9476. @article{3a7d8134df9a4f6fa8b927a82cb76026,
title = {Flapping wing drones show off their skills},
author = {Guido Croon},
url = {https://research.tudelft.nl/en/publications/flapping-wing-drones-show-off-their-skills},
doi = {10.1126/scirobotics.abd0233},
issn = {2470-9476},
year = {2020},
date = {2020-07-22},
journal = {Science Robotics},
volume = {5},
number = {44},
publisher = {American Association for the Advancement of Science},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Guido de Croon Flapping wing drones show off their skills (Journal Article) In: Science Robotics, vol. 5, no. 24, 2020. @article{commentary_science,
title = {Flapping wing drones show off their skills},
author = {Guido de Croon},
url = {http://robotics.sciencemag.org/cgi/content/full/5/44/eabd0233?ijkey=w4kqkh4vD3UwU&keytype=ref&siteid=robotics},
year = {2020},
date = {2020-07-22},
journal = {Science Robotics},
volume = {5},
number = {24},
abstract = {The identification and solution of a major efficiency loss in small flapping wing drones lead to more agile aerobatic maneuvers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The identification and solution of a major efficiency loss in small flapping wing drones lead to more agile aerobatic maneuvers. |
Mario Coppola; Kimberly N. McGuire; Christophe De Wagter; Guido C. H. E. Croon A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints (Journal Article) In: Frontiers In Robotics and AI, vol. 7, 2020, ISSN: 2296-9144. @article{6f7981dc9bd8476c9113bafb84540758,
title = {A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints},
author = {Mario Coppola and Kimberly N. McGuire and Christophe De Wagter and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/a-survey-on-swarming-with-micro-air-vehicles-fundamental-challeng},
doi = {10.3389/frobt.2020.00018},
issn = {2296-9144},
year = {2020},
date = {2020-02-25},
journal = {Frontiers In Robotics and AI},
volume = {7},
publisher = {Frontiers Media},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Mario Coppola, Kimberly N. McGuire, Christophe De Wagter; Guido C. H. E. de Croon A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints (Journal Article) In: Frontiers in Robotics and AI, 2020. @article{frontiers_survey,
title = {A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints},
author = { Mario Coppola, Kimberly N. McGuire, Christophe De Wagter and Guido C. H. E. de Croon},
url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.00018/full},
year = {2020},
date = {2020-02-25},
journal = {Frontiers in Robotics and AI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Kirk Y. W. Scheper; Guido Croon Evolution of robust high speed optical-flow-based landing for autonomous MAVs (Journal Article) In: Robotics and Autonomous Systems, vol. 124, 2020, ISSN: 0921-8890. @article{9a283c0ce100493190e8b39ed1bad552,
title = {Evolution of robust high speed optical-flow-based landing for autonomous MAVs},
author = {Kirk Y. W. Scheper and Guido Croon},
url = {https://research.tudelft.nl/en/publications/evolution-of-robust-high-speed-optical-flow-based-landing-for-aut},
doi = {10.1016/j.robot.2019.103380},
issn = {0921-8890},
year = {2020},
date = {2020-02-01},
journal = {Robotics and Autonomous Systems},
volume = {124},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Diana A. Olejnik; Bardienus P. Duisterhof; Matej Karásek; Kirk Y. W. Scheper; Tom Van Dijk; Guido C. H. E. De Croon A Tailless Flapping Wing MAV Performing Monocular Visual Servoing Tasks (Journal Article) In: Unmanned Systems, vol. 8, no. 4, pp. 287–294, 2020, ISSN: 2301-3850. @article{8c20c8bdc06f4e7b81bcbc4b52319049,
title = {A Tailless Flapping Wing MAV Performing Monocular Visual Servoing Tasks},
author = {Diana A. Olejnik and Bardienus P. Duisterhof and Matej Karásek and Kirk Y. W. Scheper and Tom Van Dijk and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/a-tailless-flapping-wing-mav-performing-monocular-visual-servoing},
doi = {10.1142/S2301385020500235},
issn = {2301-3850},
year = {2020},
date = {2020-01-01},
journal = {Unmanned Systems},
volume = {8},
number = {4},
pages = {287–294},
publisher = {World Scientific Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Marija Popovic; Florian Thomas; Sotiris Papatheodorou; Nils Funk; Teresa Vidal-Calleja; Stefan Leutenegger Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation (Journal Article) In: 2020. @article{2012.03023,
title = {Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation},
author = {Marija Popovic and Florian Thomas and Sotiris Papatheodorou and Nils Funk and Teresa Vidal-Calleja and Stefan Leutenegger},
url = {https://arxiv.org/abs/2012.03023},
doi = {10.1109/LRA.2021.3070308},
year = {2020},
date = {2020-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Borrdephong Rattanagraikanakorn; Michiel Schuurman; Derek I. Gransden; Riender Happee; Christophe De Wagter; Alexei Sharpanskykh; Henk A. P. Blom Modelling head injury due to unmanned aircraft systems collision: Crash dummy vs human body (Journal Article) In: International Journal of Crashworthiness, vol. 27, no. 2, pp. 400–413, 2020, ISSN: 1358-8265. @article{203dfada656a40cfbf6650a5c9583782,
title = {Modelling head injury due to unmanned aircraft systems collision: Crash dummy vs human body},
author = {Borrdephong Rattanagraikanakorn and Michiel Schuurman and Derek I. Gransden and Riender Happee and Christophe De Wagter and Alexei Sharpanskykh and Henk A. P. Blom},
url = {https://research.tudelft.nl/en/publications/modelling-head-injury-due-to-unmanned-aircraft-systems-collision–2},
doi = {10.1080/13588265.2020.1807687},
issn = {1358-8265},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Crashworthiness},
volume = {27},
number = {2},
pages = {400–413},
publisher = {Taylor and Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Yiduo Wang; Nils Funk; Milad Ramezani; Sotiris Papatheodorou; Marija Popovic; Marco Camurri; Stefan Leutenegger; Maurice Fallon Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks (Journal Article) In: 2020. @article{2010.09232,
title = {Elastic and Efficient LiDAR Reconstruction for Large-Scale Exploration Tasks},
author = {Yiduo Wang and Nils Funk and Milad Ramezani and Sotiris Papatheodorou and Marija Popovic and Marco Camurri and Stefan Leutenegger and Maurice Fallon},
url = {https://arxiv.org/abs/2010.09232},
doi = {10.1109/ICRA48506.2021.9561736},
year = {2020},
date = {2020-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Nils Funk; Juan Tarrio; Sotiris Papatheodorou; Marija Popovic; Pablo F. Alcantarilla; Stefan Leutenegger Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning (Journal Article) In: 2020. @article{2010.07929,
title = {Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning},
author = {Nils Funk and Juan Tarrio and Sotiris Papatheodorou and Marija Popovic and Pablo F. Alcantarilla and Stefan Leutenegger},
url = {https://arxiv.org/abs/2010.07929},
doi = {10.1109/LRA.2021.3061989},
year = {2020},
date = {2020-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Nikhil D. Potdar; Guido C. H. E. Croon; Javier Alonso-Mora Online trajectory planning and control of a MAV payload system in dynamic environments (Journal Article) In: Autonomous Robots, vol. 44, no. 6, pp. 1065–1089, 2020, ISSN: 0929-5593. @article{6232384ed01947c7b1bc000e202acdec,
title = {Online trajectory planning and control of a MAV payload system in dynamic environments},
author = {Nikhil D. Potdar and Guido C. H. E. Croon and Javier Alonso-Mora},
url = {https://research.tudelft.nl/en/publications/online-trajectory-planning-and-control-of-a-mav-payload-system-in},
doi = {10.1007/s10514-020-09919-8},
issn = {0929-5593},
year = {2020},
date = {2020-01-01},
journal = {Autonomous Robots},
volume = {44},
number = {6},
pages = {1065–1089},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sihao Sun; X. Wang; Q. P. Chu; C. C. Visser Incremental Nonlinear Fault-Tolerant Control of a Quadrotor With Complete Loss of Two Opposing Rotors (Journal Article) In: IEEE Transactions on Robotics, vol. 37, no. 1, pp. 116–130, 2020, ISSN: 1552-3098, (Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.). @article{SunEtAl2020,
title = {Incremental Nonlinear Fault-Tolerant Control of a Quadrotor With Complete Loss of Two Opposing Rotors},
author = {Sihao Sun and X. Wang and Q. P. Chu and C. C. Visser},
url = {https://research.tudelft.nl/en/publications/incremental-nonlinear-fault-tolerant-control-of-a-quadrotor-with-},
doi = {10.1109/TRO.2020.3010626},
issn = {1552-3098},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Robotics},
volume = {37},
number = {1},
pages = {116–130},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
note = {Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Ewoud Smeur; Murat Bronz; Guido Croon Incremental Control and Guidance of Hybrid Aircraft Applied to a Tailsitter Unmanned Air Vehicle (Journal Article) In: Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control, vol. 43, no. 2, pp. 274–287, 2020, ISSN: 0731-5090. @article{28cd0fb3e4134c99a0b9b01f94dac72e,
title = {Incremental Control and Guidance of Hybrid Aircraft Applied to a Tailsitter Unmanned Air Vehicle},
author = {Ewoud Smeur and Murat Bronz and Guido Croon},
url = {https://research.tudelft.nl/en/publications/incremental-control-and-guidance-of-hybrid-aircraft-applied-to-a-},
doi = {10.2514/1.G004520},
issn = {0731-5090},
year = {2020},
date = {2020-01-01},
journal = {Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control},
volume = {43},
number = {2},
pages = {274–287},
publisher = {American Institute of Aeronautics and Astronautics Inc. (AIAA)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Maria J. Knol; Hieab H. H. Adams; José Rafael J. Romero; Erik B. Van Den Akker; Shuo Li; Sven J. Van Der Lee; Jeroen Van Der Grond; Christopher Chen; Meike W. Vernooij; More Authors Association of common genetic variants with brain microbleeds: A genome-wide association study (Journal Article) In: Neurology, vol. 95, no. 24, pp. E3331–E3343, 2020, ISSN: 0028-3878. @article{40f59ed3cf414e88abca9b725dd0993d,
title = {Association of common genetic variants with brain microbleeds: A genome-wide association study},
author = {Maria J. Knol and Hieab H. H. Adams and José Rafael J. Romero and Erik B. Van Den Akker and Shuo Li and Sven J. Van Der Lee and Jeroen Van Der Grond and Christopher Chen and Meike W. Vernooij and More Authors},
url = {https://research.tudelft.nl/en/publications/association-of-common-genetic-variants-with-brain-microbleeds-a-g},
doi = {10.1212/WNL.0000000000010852},
issn = {0028-3878},
year = {2020},
date = {2020-01-01},
journal = {Neurology},
volume = {95},
number = {24},
pages = {E3331–E3343},
publisher = {Lippincott Williams and Wilkins},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Whereas insects seem to control their flight effortlessly with the help of optical flow, robots that attempt to do the same run into problems. We propose as a solution a learning process that allows robots to see distances by means of visual appearance (colors, textures, shapes). Would it be possible that insects do the same?
