2022
|
Masters Theses
|
Pietro Campolucci Model and Actuator Based Trajectory Tracking for Incremental Nonlinear Dynamic Inversion Controllers (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Mancinelli, A. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:41895fac-aa59-47db-9c01-5e2879460b57,
title = {Model and Actuator Based Trajectory Tracking for Incremental Nonlinear Dynamic Inversion Controllers},
author = {Pietro Campolucci},
url = {http://resolver.tudelft.nl/uuid:41895fac-aa59-47db-9c01-5e2879460b57},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions.},
note = {Smeur, E.J.J. (mentor); Mancinelli, A. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions. |
Alejandro Barberia Chueca Onboard Drone Detection with Event Cameras (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:be142c0a-3475-4571-b9c5-9118d397c51a,
title = {Onboard Drone Detection with Event Cameras},
author = {Alejandro Barberia Chueca},
url = {http://resolver.tudelft.nl/uuid:be142c0a-3475-4571-b9c5-9118d397c51a},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In an effort to develop a new relative sensing method for drone swarms, the suitability of event cameras is assessed for propeller detection. Benchmark tests were conducted for different propellers under different lighting and background conditions, varying the observation distance and spinning frequency. The different tests were evaluated on event count, frequency, and clustering, as these are the most characteristic properties of the propeller-generated signal. A propeller detection metric was derived as a fuzzy classifier to assess detectability. It was observed that the sensor employed is limiting the detection range due to low resolution, with a maximum detection range of 75 cm. While at low spinning frequencies it is possible to detect the propeller at such distance, for higher frequences (6000 to 8000 RPMs) the range decreases to 45 cm for the tests with highest blade to background contrast and two-blade propellers. It was observed that lower contrasts reduce the successful detections only to low frequencies, and three-blade propellers become completely indetectable due to the static overlap between the blades. Therefore, it is concluded that, at this stage of the technology, the use case of event cameras for relative sensing is constrained to close distances with high contrast.},
note = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In an effort to develop a new relative sensing method for drone swarms, the suitability of event cameras is assessed for propeller detection. Benchmark tests were conducted for different propellers under different lighting and background conditions, varying the observation distance and spinning frequency. The different tests were evaluated on event count, frequency, and clustering, as these are the most characteristic properties of the propeller-generated signal. A propeller detection metric was derived as a fuzzy classifier to assess detectability. It was observed that the sensor employed is limiting the detection range due to low resolution, with a maximum detection range of 75 cm. While at low spinning frequencies it is possible to detect the propeller at such distance, for higher frequences (6000 to 8000 RPMs) the range decreases to 45 cm for the tests with highest blade to background contrast and two-blade propellers. It was observed that lower contrasts reduce the successful detections only to low frequencies, and three-blade propellers become completely indetectable due to the static overlap between the blades. Therefore, it is concluded that, at this stage of the technology, the use case of event cameras for relative sensing is constrained to close distances with high contrast. |
Yvonne Eggers Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b,
title = {Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation},
author = {Yvonne Eggers},
url = {http://resolver.tudelft.nl/uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates.},
note = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates. |
Robin Ferede An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c,
title = {An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers},
author = {Robin Ferede},
url = {http://resolver.tudelft.nl/uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Developing optimal controllers for aggressive high speed quadcopter flight remains a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning are a good candidate for real-time optimal quadcopter control. In these methods, the networks (termed G&CNets) are trained using optimal trajectories obtained from a dynamical model of the quadcopter by means of a direct transcription method. A major problem with these methods is the effects of unmodeled dynamics. In this work we identify these effects for G&CNets trained for power optimal full state-to-rpm feedback. We propose an adaptive control strategy to mitigate the effects of unmodeled roll, pitch and yaw moments. Our method works by generating optimal trajectories with constant external moments added to the model and training a network to learn the policy that maps state and external moments to the corresponding optimal rpm command. We demonstrate the effectiveness of our method by performing power-optimal hover-to-hover flights with and without moment feedback. The flight tests show that the inclusion of this moment feedback significantly improves the controller's performance. Additionally we compare the adaptive controller's performance to a time optimal Bang-Bang controller for consecutive waypoint flight and show significantly faster lap times on a 3x4m track.},
note = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Developing optimal controllers for aggressive high speed quadcopter flight remains a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning are a good candidate for real-time optimal quadcopter control. In these methods, the networks (termed G&CNets) are trained using optimal trajectories obtained from a dynamical model of the quadcopter by means of a direct transcription method. A major problem with these methods is the effects of unmodeled dynamics. In this work we identify these effects for G&CNets trained for power optimal full state-to-rpm feedback. We propose an adaptive control strategy to mitigate the effects of unmodeled roll, pitch and yaw moments. Our method works by generating optimal trajectories with constant external moments added to the model and training a network to learn the policy that maps state and external moments to the corresponding optimal rpm command. We demonstrate the effectiveness of our method by performing power-optimal hover-to-hover flights with and without moment feedback. The flight tests show that the inclusion of this moment feedback significantly improves the controller's performance. Additionally we compare the adaptive controller's performance to a time optimal Bang-Bang controller for consecutive waypoint flight and show significantly faster lap times on a 3x4m track. |
Jan Verheyen Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation? (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca,
title = {Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation?},
author = {Jan Verheyen},
url = {http://resolver.tudelft.nl/uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form.},
note = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form. |
Erik Oever An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300,
title = {An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies},
author = {Erik Oever},
url = {http://resolver.tudelft.nl/uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source.},
note = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source. |
Shawn Schröter We fly as one: Design and Joint Control of a Conjoined Biplane and Quadrotor (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29,
title = {We fly as one: Design and Joint Control of a Conjoined Biplane and Quadrotor},
author = {Shawn Schröter},
url = {http://resolver.tudelft.nl/uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Unmanned Aerial Vehicles, UAVs, serve many purposes<br/>these days, such as short-range inspections<br/>and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.<br/><br/>This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.<br/><br/>This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight<br/>release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:<br/>a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.<br/>In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies<br/>are of minimal influence. However, the angular rate damper<br/>strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.<br},
note = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Unmanned Aerial Vehicles, UAVs, serve many purposes<br/>these days, such as short-range inspections<br/>and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.<br/><br/>This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.<br/><br/>This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight<br/>release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:<br/>a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.<br/>In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies<br/>are of minimal influence. However, the angular rate damper<br/>strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.<br |
Jingyi LU Evolving-to-Learn with Spiking Neural Networks (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6,
title = {Evolving-to-Learn with Spiking Neural Networks},
author = {Jingyi LU},
url = {http://resolver.tudelft.nl/uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This thesis aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. In this work, we first test the algorithm in basic binary pattern classification tasks. Then, using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.},
note = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This thesis aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. In this work, we first test the algorithm in basic binary pattern classification tasks. Then, using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature. |
Tommy Tran Semantic Segmentation using Deep Neural Networks for MAVs (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Xu, Y. (mentor); de Wagter, C. (graduation committee); van Gemert, J.C. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:7735d01c-b4cd-4173-a584-652f269c078c,
title = {Semantic Segmentation using Deep Neural Networks for MAVs},
author = {Tommy Tran},
url = {http://resolver.tudelft.nl/uuid:7735d01c-b4cd-4173-a584-652f269c078c},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer.},
note = {de Croon, G.C.H.E. (mentor); Xu, Y. (mentor); de Wagter, C. (graduation committee); van Gemert, J.C. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer. |
Chris Groen Grammatical Evolution for Optimising Drone Behaviors (Masters Thesis) TU Delft Aerospace Engineering, 2022, (Li, S. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6,
title = {Grammatical Evolution for Optimising Drone Behaviors},
author = {Chris Groen},
url = {http://resolver.tudelft.nl/uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors.},
note = {Li, S. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors. |
Roelof Stikker Self-supervised finetuning of stereo matching algorithms (Masters Thesis) TU Delft Aerospace Engineering, 2022, (de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:a4305f40-c095-45c2-bc7e-84e23efa70d6,
title = {Self-supervised finetuning of stereo matching algorithms},
author = {Roelof Stikker},
url = {http://resolver.tudelft.nl/uuid:a4305f40-c095-45c2-bc7e-84e23efa70d6},
year = {2022},
date = {2022-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Abstract— Stereo vision is a commonly applied method to achieve depth perception on Micro Air Vehicles (MAVs). Stereo matching algorithms are often optimized for specific environments and camera properties, using the ground truth error as a supervisor. However, in practical applications ground truth data is usually not available. Therefore, in this research, we finetune several conventional stereo matching algorithms (BM, SGBM, and ELAS) and a neural network (AnyNet) using self-supervision. The settings of the conventional algorithms are optimized with NSGA-II, using the reconstruction error and disparity density as objective functions. AnyNet is finetuned with the reconstruction error, as well as with the disparity map of conventional methods. We conclude that finetuning the parameters of conventional stereo algorithms using the reconstruction error can lead to a slight improvement in performance compared with the general settings, depending on the stereo algorithm. The performance of the conventional methods is comparable to that of AnyNet on a major portion of the image. However, removing the values with low confidence in the disparity map of ELAS and interpolating the missing disparities leads to an accuracy well above AnyNet.},
note = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Abstract— Stereo vision is a commonly applied method to achieve depth perception on Micro Air Vehicles (MAVs). Stereo matching algorithms are often optimized for specific environments and camera properties, using the ground truth error as a supervisor. However, in practical applications ground truth data is usually not available. Therefore, in this research, we finetune several conventional stereo matching algorithms (BM, SGBM, and ELAS) and a neural network (AnyNet) using self-supervision. The settings of the conventional algorithms are optimized with NSGA-II, using the reconstruction error and disparity density as objective functions. AnyNet is finetuned with the reconstruction error, as well as with the disparity map of conventional methods. We conclude that finetuning the parameters of conventional stereo algorithms using the reconstruction error can lead to a slight improvement in performance compared with the general settings, depending on the stereo algorithm. The performance of the conventional methods is comparable to that of AnyNet on a major portion of the image. However, removing the values with low confidence in the disparity map of ELAS and interpolating the missing disparities leads to an accuracy well above AnyNet. |
Miscellaneous
|
Julius Rückin; Liren Jin; Federico Magistri; Cyrill Stachniss; Marija Popović Informative Path Planning for Active Learning in Aerial Semantic Mapping (Miscellaneous) 2022. @misc{2203.01652,
title = {Informative Path Planning for Active Learning in Aerial Semantic Mapping},
author = {Julius Rückin and Liren Jin and Federico Magistri and Cyrill Stachniss and Marija Popović},
url = {https://arxiv.org/abs/2203.01652},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Felix Stache; Jonas Westheider; Federico Magistri; Cyrill Stachniss; Marija Popović Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation (Miscellaneous) 2022. @misc{2203.01642,
title = {Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation},
author = {Felix Stache and Jonas Westheider and Federico Magistri and Cyrill Stachniss and Marija Popović},
url = {https://arxiv.org/abs/2203.01642},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Yilun Wu; Federico Paredes-Vallés; Guido C. H. E. Croon Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes (Miscellaneous) 2022. @misc{2211.13726,
title = {Rethinking Event-based Optical Flow: Iterative Deblurring as an Alternative to Correlation Volumes},
author = {Yilun Wu and Federico Paredes-Vallés and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2211.13726},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Rik J. Bouwmeester; Federico Paredes-Vallés; Guido C. H. E. Croon NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter (Miscellaneous) 2022. @misc{2209.06918,
title = {NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter},
author = {Rik J. Bouwmeester and Federico Paredes-Vallés and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2209.06918},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Yingfu Xu; Guido C. H. E. Croon CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry (Miscellaneous) 2022. @misc{2208.13935,
title = {CUAHN-VIO: Content-and-Uncertainty-Aware Homography Network for Visual-Inertial Odometry},
author = {Yingfu Xu and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2208.13935},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sabrina M. Neuman; Brian Plancher; Bardienus P. Duisterhof; Srivatsan Krishnan; Colby Banbury; Mark Mazumder; Shvetank Prakash; Jason Jabbour; Aleksandra Faust; Guido C. H. E. Croon; Vijay Janapa Reddi Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots (Miscellaneous) 2022. @misc{2205.05748,
title = {Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots},
author = {Sabrina M. Neuman and Brian Plancher and Bardienus P. Duisterhof and Srivatsan Krishnan and Colby Banbury and Mark Mazumder and Shvetank Prakash and Jason Jabbour and Aleksandra Faust and Guido C. H. E. Croon and Vijay Janapa Reddi},
url = {https://arxiv.org/abs/2205.05748},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Cheng Liu; Erik-Jan Kampen; Guido C. H. E. Croon Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning (Miscellaneous) 2022. @misc{2203.14749,
title = {Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning},
author = {Cheng Liu and Erik-Jan Kampen and Guido C. H. E. Croon},
url = {https://arxiv.org/abs/2203.14749},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Diana A. Olejnik; Sunyi Wang; Julien Dupeyroux; Stein Stroobants; Matěj Karásek; Christophe De Wagter; Guido Croon An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System (Miscellaneous) 2022. @misc{2202.06723,
title = {An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System},
author = {Diana A. Olejnik and Sunyi Wang and Julien Dupeyroux and Stein Stroobants and Matěj Karásek and Christophe De Wagter and Guido Croon},
url = {https://arxiv.org/abs/2202.06723},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Tommy Tran; Yingfu Xu; Guido Croon Data underlying the publication: "Semantic Segmentation over Time using Deep Neural Networks" (Miscellaneous) 2022. @misc{Tran2022,
title = {Data underlying the publication: "Semantic Segmentation over Time using Deep Neural Networks"},
author = {Tommy Tran and Yingfu Xu and Guido Croon},
url = {https://data.4tu.nl/articles/dataset/Data_underlying_the_publication_Semantic_Segmentation_using_Deep_Neural_Networks_for_MAVs_/19042235},
doi = {10.4121/19042235.v2},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Sven Pfeiffer; Shushuai Li; Guido Croon Flight data underlying the publication: "Three-dimensional Relative Localization and Synchronized Movement with Wireless Ranging" (Miscellaneous) 2022. @misc{Pfeiffer2022,
title = {Flight data underlying the publication: "Three-dimensional Relative Localization and Synchronized Movement with Wireless Ranging"},
author = {Sven Pfeiffer and Shushuai Li and Guido Croon},
url = {https://data.4tu.nl/articles/dataset/Flight_data_underlying_the_publication_Three-dimensional_Relative_Localization_and_Swarming_with_Wireless_Ranging_/17372348},
doi = {10.4121/17372348.v2},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
Yifu Tao; Marija Popović; Yiduo Wang; Sundara Tejaswi Digumarti; Nived Chebrolu; Maurice Fallon 3D Lidar Reconstruction with Probabilistic Depth Completion for Robotic Navigation (Miscellaneous) 2022. @misc{2207.12520,
title = {3D Lidar Reconstruction with Probabilistic Depth Completion for Robotic Navigation},
author = {Yifu Tao and Marija Popović and Yiduo Wang and Sundara Tejaswi Digumarti and Nived Chebrolu and Maurice Fallon},
url = {https://arxiv.org/abs/2207.12520},
year = {2022},
date = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
PhD Theses
|
C. Wagter Hover and fast flight of minimum-mass mission-capable flying robots (PhD Thesis) Delft University of Technology, 2022, ISBN: 978-94-6384-333-1. @phdthesis{3d15049bf69542d8b8d18d4bac1c8abd,
title = {Hover and fast flight of minimum-mass mission-capable flying robots},
author = {C. Wagter},
url = {https://research.tudelft.nl/en/publications/hover-and-fast-flight-of-minimum-mass-mission-capable-flying-robo},
doi = {10.4233/uuid:3d15049b-f695-42d8-b8d1-8d4bac1c8abd},
isbn = {978-94-6384-333-1},
year = {2022},
date = {2022-01-01},
school = {Delft University of Technology},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
|
2021
|
Journal Articles
|
D. Wijnker, T. van Dijk, G.C.H.E. de Croon, C. De Wagter Hear-and-avoid for unmanned air vehicles using convolutional neural networks (Journal Article) In: International Journal of Micro Air Vehicles, vol. 13, pp. 1-15, 2021. @article{audio_ijmav_2021,
title = { Hear-and-avoid for unmanned air vehicles using convolutional neural networks},
author = {D. Wijnker, T. van Dijk, G.C.H.E. de Croon, C. De Wagter},
url = {https://journals.sagepub.com/doi/full/10.1177/1756829321992137},
doi = {10.1177/1756829321992137},
year = {2021},
date = {2021-02-10},
journal = {International Journal of Micro Air Vehicles},
volume = {13},
pages = {1-15},
abstract = {To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to aircraft, the data set has to be artificially produced, so the unmanned air vehicle sound is captured separately from the aircraft sound. They are then mixed with unmanned air vehicle recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a convolutional neural network that uses the features Mel frequency cepstral coefficient, spectrogram or Mel spectrogram as input. For each feature, the effect of unmanned air vehicle/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings are explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the unmanned air vehicle/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. Finally, the data set is provided as open access.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to aircraft, the data set has to be artificially produced, so the unmanned air vehicle sound is captured separately from the aircraft sound. They are then mixed with unmanned air vehicle recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a convolutional neural network that uses the features Mel frequency cepstral coefficient, spectrogram or Mel spectrogram as input. For each feature, the effect of unmanned air vehicle/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings are explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the unmanned air vehicle/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. Finally, the data set is provided as open access. |
G.C.H.E. de Croon, C. De Wagter, T. Seidl Enhancing optical-flow-based control by learning visual appearance cues for flying robots (Journal Article) In: Nature Machine Intelligence, vol. 3, no. 1, 2021. @article{nature_ai_optical_flow,
title = {Enhancing optical-flow-based control by learning visual appearance cues for flying robots},
author = {G.C.H.E. de Croon, C. De Wagter, T. Seidl},
url = {https://www.nature.com/articles/s42256-020-00279-7},
doi = {10.1038/s42256-020-00279-7},
year = {2021},
date = {2021-01-19},
urldate = {2021-01-19},
journal = {Nature Machine Intelligence},
volume = {3},
number = {1},
abstract = {Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes. |
D. C. Wijnker; Tom Dijk; M. Snellen; G. C. H. E. Croon; C. Wagter Hear-and-avoid for unmanned air vehicles using convolutional neural networks (Journal Article) In: International Journal of Micro Air Vehicles, vol. 13, 2021, ISSN: 1756-8293. @article{5a62c555af994d55a695fa3044e2e37c,
title = {Hear-and-avoid for unmanned air vehicles using convolutional neural networks},
author = {D. C. Wijnker and Tom Dijk and M. Snellen and G. C. H. E. Croon and C. Wagter},
url = {https://research.tudelft.nl/en/publications/hear-and-avoid-for-unmanned-air-vehicles-using-convolutional-neur},
doi = {10.1177/1756829321992137},
issn = {1756-8293},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Micro Air Vehicles},
volume = {13},
publisher = {Multi-Science Publishing Co. Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
G. C. H. E. Croon; C. Wagter; T. Seidl Enhancing optical-flow-based control by learning visual appearance cues for flying robots (Journal Article) In: Nature Machine Intelligence, vol. 3, no. 1, pp. 33–41, 2021, ISSN: 2522-5839. @article{de4078484028493ea9cdce4a6c255a63,
title = {Enhancing optical-flow-based control by learning visual appearance cues for flying robots},
author = {G. C. H. E. Croon and C. Wagter and T. Seidl},
url = {https://research.tudelft.nl/en/publications/enhancing-optical-flow-based-control-by-learning-visual-appearanc},
doi = {10.1038/s42256-020-00279-7},
issn = {2522-5839},
year = {2021},
date = {2021-01-01},
journal = {Nature Machine Intelligence},
volume = {3},
number = {1},
pages = {33–41},
publisher = {Springer Nature},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Patricia P. Parlevliet; Andrey Kanaev; Chou P. Hung; Andreas Schweiger; Frederick D. Gregory; Ryad Benosman; Guido C. H. E. Croon; Yoram Gutfreund; Chung Chuan Lo; Cynthia F. Moss Autonomous Flying With Neuromorphic Sensing (Journal Article) In: Frontiers in Neuroscience, vol. 15, 2021, ISSN: 1662-4548. @article{64b98a51388b454fb60cf6e9e02842c8,
title = {Autonomous Flying With Neuromorphic Sensing},
author = {Patricia P. Parlevliet and Andrey Kanaev and Chou P. Hung and Andreas Schweiger and Frederick D. Gregory and Ryad Benosman and Guido C. H. E. Croon and Yoram Gutfreund and Chung Chuan Lo and Cynthia F. Moss},
url = {https://research.tudelft.nl/en/publications/autonomous-flying-with-neuromorphic-sensing},
doi = {10.3389/fnins.2021.672161},
issn = {1662-4548},
year = {2021},
date = {2021-01-01},
journal = {Frontiers in Neuroscience},
volume = {15},
publisher = {Frontiers Media},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Pulkit Goyal; Antoine Cribellier; Guido C. H. E. Croon; Martin J. Lankheet; Johan L. Leeuwen; Remco P. M. Pieters; Florian T. Muijres Bumblebees land rapidly and robustly using a sophisticated modular flight control strategy (Journal Article) In: iScience, vol. 24, no. 5, 2021, ISSN: 2589-0042. @article{b8ea5aa24527426491652d2a82f84732,
title = {Bumblebees land rapidly and robustly using a sophisticated modular flight control strategy},
author = {Pulkit Goyal and Antoine Cribellier and Guido C. H. E. Croon and Martin J. Lankheet and Johan L. Leeuwen and Remco P. M. Pieters and Florian T. Muijres},
url = {https://research.tudelft.nl/en/publications/bumblebees-land-rapidly-and-robustly-using-a-sophisticated-modula},
doi = {10.1016/j.isci.2021.102407},
issn = {2589-0042},
year = {2021},
date = {2021-01-01},
journal = {iScience},
volume = {24},
number = {5},
publisher = {Cell Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
C. De Wagter; B. Remes; E. Smeur; F. Tienen; R. Ruijsink; K. Hecke; E. Horst The NederDrone: A hybrid lift, hybrid energy hydrogen UAV (Journal Article) In: International Journal of Hydrogen Energy, vol. 46, no. 29, pp. 16003–16018, 2021, ISSN: 0360-3199. @article{0bbd9df59c4842f2b3a6284876642d15,
title = {The NederDrone: A hybrid lift, hybrid energy hydrogen UAV},
author = {C. De Wagter and B. Remes and E. Smeur and F. Tienen and R. Ruijsink and K. Hecke and E. Horst},
url = {https://research.tudelft.nl/en/publications/the-nederdrone-a-hybrid-lift-hybrid-energy-hydrogen-uav},
doi = {10.1016/j.ijhydene.2021.02.053},
issn = {0360-3199},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Hydrogen Energy},
volume = {46},
number = {29},
pages = {16003–16018},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Christophe De Wagter; Federico Paredes-Vallés; Nilay Sheth; Guido Croon The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition (Journal Article) In: 2021. @article{2109.14985,
title = {The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition},
author = {Christophe De Wagter and Federico Paredes-Vallés and Nilay Sheth and Guido Croon},
url = {https://arxiv.org/abs/2109.14985},
doi = {10.55417/fr.2022042},
year = {2021},
date = {2021-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Chris P. L. Jong; Bart D. W. Remes; Sunyou Hwang; Christophe De Wagter Never landing drone: Autonomous soaring of a unmanned aerial vehicle in front of a moving obstacle (Journal Article) In: International Journal of Micro Air Vehicles, vol. 13, 2021, ISSN: 1756-8293. @article{1989fa30b9d54c99b63424f881be428b,
title = {Never landing drone: Autonomous soaring of a unmanned aerial vehicle in front of a moving obstacle},
author = {Chris P. L. Jong and Bart D. W. Remes and Sunyou Hwang and Christophe De Wagter},
url = {https://research.tudelft.nl/en/publications/never-landing-drone-autonomous-soaring-of-a-unmanned-aerial-vehic},
doi = {10.1177/17568293211060500},
issn = {1756-8293},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Micro Air Vehicles},
volume = {13},
publisher = {Multi-Science Publishing Co. Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Poramate Manoonpong; Luca Patanè; Xiaofeng Xiong; Ilya Brodoline; Julien Dupeyroux; Stéphane Viollet; Paolo Arena; Julien R. Serres Insect-inspired robots: Bridging biological and artificial systems (Journal Article) In: Sensors, vol. 21, no. 22, 2021, ISSN: 1424-8220. @article{0c29f45f148247258d2b4b154c12b645,
title = {Insect-inspired robots: Bridging biological and artificial systems},
author = {Poramate Manoonpong and Luca Patanè and Xiaofeng Xiong and Ilya Brodoline and Julien Dupeyroux and Stéphane Viollet and Paolo Arena and Julien R. Serres},
url = {https://research.tudelft.nl/en/publications/insect-inspired-robots-bridging-biological-and-artificial-systems},
doi = {10.3390/s21227609},
issn = {1424-8220},
year = {2021},
date = {2021-01-01},
journal = {Sensors},
volume = {21},
number = {22},
publisher = {MDPI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
C. De Wagter; F. Paredes-Vallés; N. Sheth; G. Croon Learning fast in autonomous drone racing (Journal Article) In: Nature Machine Intelligence, vol. 3, no. 10, pp. 923, 2021, ISSN: 2522-5839, (Copyright: Copyright 2021 Elsevier B.V., All rights reserved.). @article{fabcc0c2a2c34c9fade7b4b03119bb55,
title = {Learning fast in autonomous drone racing},
author = {C. De Wagter and F. Paredes-Vallés and N. Sheth and G. Croon},
url = {https://research.tudelft.nl/en/publications/learning-fast-in-autonomous-drone-racing},
doi = {10.1038/s42256-021-00405-z},
issn = {2522-5839},
year = {2021},
date = {2021-01-01},
journal = {Nature Machine Intelligence},
volume = {3},
number = {10},
pages = {923},
publisher = {Springer Nature},
note = {Copyright: Copyright 2021 Elsevier B.V., All rights reserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Raoul Dinaux; Nikhil Wessendorp; Julien Dupeyroux; Guido C. H. E. De Croon FAITH: Fast Iterative Half-Plane Focus of Expansion Estimation Using Optic Flow (Journal Article) In: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7627–7634, 2021, ISSN: 2377-3766. @article{6df8a47b66df4a229bfe02ef54874887,
title = {FAITH: Fast Iterative Half-Plane Focus of Expansion Estimation Using Optic Flow},
author = {Raoul Dinaux and Nikhil Wessendorp and Julien Dupeyroux and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/faith-fast-iterative-half-plane-focus-of-expansion-estimation-usi},
doi = {10.1109/LRA.2021.3100153},
issn = {2377-3766},
year = {2021},
date = {2021-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {4},
pages = {7627–7634},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sven Pfeiffer; Christophe De Wagter; Guido C. H. E. De Croon A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors (Journal Article) In: IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6725–6732, 2021, ISSN: 2377-3766, (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{db62ab4841ed4e768c8aa61bf0e4f86f,
title = {A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors},
author = {Sven Pfeiffer and Christophe De Wagter and Guido C. H. E. De Croon},
url = {https://research.tudelft.nl/en/publications/a-computationally-efficient-moving-horizon-estimator-for-ultra-wi},
doi = {10.1109/LRA.2021.3095519},
issn = {2377-3766},
year = {2021},
date = {2021-01-01},
journal = {IEEE Robotics and Automation Letters},
volume = {6},
number = {4},
pages = {6725–6732},
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}
}
|
Ye Zhou; Hann Woei Ho; Qiping Chu Extended incremental nonlinear dynamic inversion for optical flow control of micro air vehicles (Journal Article) In: Aerospace Science and Technology, vol. 116, 2021, ISSN: 1270-9638. @article{e8893d1b300e42249a42fc879c94169b,
title = {Extended incremental nonlinear dynamic inversion for optical flow control of micro air vehicles},
author = {Ye Zhou and Hann Woei Ho and Qiping Chu},
url = {https://research.tudelft.nl/en/publications/extended-incremental-nonlinear-dynamic-inversion-for-optical-flow},
doi = {10.1016/j.ast.2021.106889},
issn = {1270-9638},
year = {2021},
date = {2021-01-01},
journal = {Aerospace Science and Technology},
volume = {116},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Seng Man Wong; Hann Woei Ho; Mohd Zulkifly Abdullah Design and Fabrication of a Dual Rotor-Embedded Wing Vertical Take-Off and Landing Unmanned Aerial Vehicle (Journal Article) In: Unmanned Systems, vol. 9, no. 1, pp. 45–63, 2021, ISSN: 2301-3850. @article{3ef0950c1f824e58b1c847e69a6adae5,
title = {Design and Fabrication of a Dual Rotor-Embedded Wing Vertical Take-Off and Landing Unmanned Aerial Vehicle},
author = {Seng Man Wong and Hann Woei Ho and Mohd Zulkifly Abdullah},
url = {https://research.tudelft.nl/en/publications/design-and-fabrication-of-a-dual-rotor-embedded-wing-vertical-tak},
doi = {10.1142/S2301385021500096},
issn = {2301-3850},
year = {2021},
date = {2021-01-01},
journal = {Unmanned Systems},
volume = {9},
number = {1},
pages = {45–63},
publisher = {World Scientific Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
H. Y. Lee; H. W. Ho; Y. Zhou Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations: Faster Region-based Convolutional Neural Network Approach (Journal Article) In: Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 101, no. 1, 2021, ISSN: 0921-0296. @article{41bbc3e560d1448093ab2ce6220da811,
title = {Deep Learning-based Monocular Obstacle Avoidance for Unmanned Aerial Vehicle Navigation in Tree Plantations: Faster Region-based Convolutional Neural Network Approach},
author = {H. Y. Lee and H. W. Ho and Y. Zhou},
url = {https://research.tudelft.nl/en/publications/deep-learning-based-monocular-obstacle-avoidance-for-unmanned-aer},
doi = {10.1007/s10846-020-01284-z},
issn = {0921-0296},
year = {2021},
date = {2021-01-01},
journal = {Journal of Intelligent and Robotic Systems: Theory and Applications},
volume = {101},
number = {1},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
data
|
Sven Pfeiffer; Christophe Wagter; Guido Croon Data underlying the publication: "A Computationally Efficient Moving Horizon Estimator for UWB Localization on Small Quadrotors"" (data) 2021. @data{https://doi.org/10.4121/14827680.v1,
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_/14827680/1},
doi = {10.4121/14827680.v1},
year = {2021},
date = {2021-01-01},
publisher = {4TU.ResearchData},
keywords = {},
pubstate = {published},
tppubtype = {data}
}
|
Christophe De Wagter; Federico Paredes-Vallés; Nilay Sheth; Guido C. H. E. De Croon Logfiles of the Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition (data) 2021. @data{https://doi.org/10.34894/ckl4tq,
title = {Logfiles of the Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition},
author = {Christophe De Wagter and Federico Paredes-Vallés and Nilay Sheth and Guido C. H. E. De Croon},
url = {https://dataverse.nl/citation?persistentId=doi:10.34894/CKL4TQ},
doi = {10.34894/CKL4TQ},
year = {2021},
date = {2021-01-01},
publisher = {DataverseNL},
keywords = {},
pubstate = {published},
tppubtype = {data}
}
|
Benjamin Keltjens; Tom Van Dijk; Guido De Croon Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes (data) 2021. @data{10.34894/kibwfc,
title = {Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes},
author = {Benjamin Keltjens and Tom Van Dijk and Guido De Croon},
url = {https://dataverse.nl/citation?persistentId=doi:10.34894/KIBWFC},
doi = {10.34894/KIBWFC},
year = {2021},
date = {2021-01-01},
publisher = {DataverseNL},
keywords = {},
pubstate = {published},
tppubtype = {data}
}
|
Guido De Croon; Christophe De Wagter; Tobias Seidl Replication Data for: "Enhancing optical flow-based control by learning visual appearance cues for flying robots" (data) 2021. @data{10.34894/klkp1m,
title = {Replication Data for: "Enhancing optical flow-based control by learning visual appearance cues for flying robots"},
author = {Guido De Croon and Christophe De Wagter and Tobias Seidl},
url = {https://dataverse.nl/citation?persistentId=doi:10.34894/KLKP1M},
doi = {10.34894/KLKP1M},
year = {2021},
date = {2021-01-01},
publisher = {DataverseNL},
keywords = {},
pubstate = {published},
tppubtype = {data}
}
|
Proceedings Articles
|
M. Gossye; S. Hwang; B. D. W. Remes Developing a modular tool to simulate regeneration power potential using orographic wind-hovering uavs (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 116–123, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{9cee2bfb6e0d475a83b8afcd52e8f69f,
title = {Developing a modular tool to simulate regeneration power potential using orographic wind-hovering uavs},
author = {M. Gossye and S. Hwang and B. D. W. Remes},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/developing-a-modular-tool-to-simulate-regeneration-power-potentia-2},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {116–123},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
G. Gonzalez Archundia; G. C. H. E. Croon; D. A. Olejnik; M. Karasek Position controller for a flapping wing drone using uwb (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 85–92, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{ecb50cf0e6cc4373932035df09604749,
title = {Position controller for a flapping wing drone using uwb},
author = {G. Gonzalez Archundia and G. C. H. E. Croon and D. A. Olejnik and M. Karasek},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/position-controller-for-a-flapping-wing-drone-using-uwb-2},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {85–92},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
E. D. Vroon; Jim Rojer; G. C. H. E. Croon Motion-based mav detection in gps-denied environments (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 42–49, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{2c157cfccbba4651801c4ddcd5e46e3f,
title = {Motion-based mav detection in gps-denied environments},
author = {E. D. Vroon and Jim Rojer and G. C. H. E. Croon},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/motion-based-mav-detection-in-gps-denied-environments},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {42–49},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
D. C. Wijngaarden; E. J. J. Smeur; B. D. W. Remes Flight code convergence: fixedwing, rotorcraft, hybrid (Proceedings Article) In: 12th International Micro Air Vehicle Conference, pp. 21–27, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{35c4b1cbfa7e4776a59740eba912776f,
title = {Flight code convergence: fixedwing, rotorcraft, hybrid},
author = {D. C. Wijngaarden and E. J. J. Smeur and B. D. W. Remes},
url = {https://research.tudelft.nl/en/publications/flight-code-convergence-fixedwing-rotorcraft-hybrid},
year = {2021},
date = {2021-01-01},
booktitle = {12th International Micro Air Vehicle Conference},
pages = {21–27},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
L. F. A. Dellemann; C. Wagter Hybrid UAV Attitude Control using INDI and Dynamic Tilt-Twist (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 131–136, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{acaf7267c5bd410fb20346333bdea387,
title = {Hybrid UAV Attitude Control using INDI and Dynamic Tilt-Twist},
author = {L. F. A. Dellemann and C. Wagter},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/hybrid-uav-attitude-control-using-indi-and-dynamic-tilt-twist},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {131–136},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
J. M. Westenberger; C. Wagter; G. C. H. E. Croon Onboard Time-Optimal Control for Tiny Quadcopters (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 93–100, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{dcdd83ec14574013abe6ff37bcbcee04,
title = {Onboard Time-Optimal Control for Tiny Quadcopters},
author = {J. M. Westenberger and C. Wagter and G. C. H. E. Croon},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/onboard-time-optimal-control-for-tiny-quadcopters},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {93–100},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
H. J. Karssies; C. Wagter XINCA: Extended Incremental Non-linear Control Allocation on the TU Delft Quadplane (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 74–84, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{a0639ab725c0448ba2f4a9cc77f02c00,
title = {XINCA: Extended Incremental Non-linear Control Allocation on the TU Delft Quadplane},
author = {H. J. Karssies and C. Wagter},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/xinca-extended-incremental-non-linear-control-allocation-on-the-t},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {74–84},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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?
