2022
|
Miscellaneous
|
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},
urldate = {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}
}
|
Bachelor Theses
|
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 (Bachelor Thesis) 2021. @bachelorthesis{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},
urldate = {2021-01-01},
publisher = {DataverseNL},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
|
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}
}
|
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}
}
|
S. Li; C. Wagter; G. C. H. E. Croon Nonlinear model predictive control for improving range-based relative localization by maximizing observability (Proceedings Article) In: Martinez-Carranza, Jose (Ed.): Proceedings of the 12th International Micro Air Vehicle Conference, pp. 28–34, 2021, (12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021). @inproceedings{b592bef42a74410d9118080d75f09dc1,
title = {Nonlinear model predictive control for improving range-based relative localization by maximizing observability},
author = {S. Li and C. Wagter and G. C. H. E. Croon},
editor = {Jose Martinez-Carranza},
url = {https://research.tudelft.nl/en/publications/nonlinear-model-predictive-control-for-improving-range-based-rela},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th International Micro Air Vehicle Conference},
pages = {28–34},
note = {12th International Micro Air Vehicle Conference, IMAV 2021 ; Conference date: 17-11-2021 Through 19-11-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Daniel Willemsen; Mario Coppola; Guido C. H. E. Croon MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models (Proceedings Article) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 5635–5640, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{d3f8712a70684f63812db25c62c65604,
title = {MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models},
author = {Daniel Willemsen and Mario Coppola and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/mambpo-sample-efficient-multi-robot-reinforcement-learning-using-},
doi = {10.1109/IROS51168.2021.9635836},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {5635–5640},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Nikhil Wessendorp; Raoul Dinaux; Julien Dupeyroux; Guido C. H. E. Croon Obstacle Avoidance onboard MAVs using a FMCW Radar (Proceedings Article) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 117–122, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{b721b22bbeb84ee5a17bebd74fc0d962,
title = {Obstacle Avoidance onboard MAVs using a FMCW Radar},
author = {Nikhil Wessendorp and Raoul Dinaux and Julien Dupeyroux and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/obstacle-avoidance-onboard-mavs-using-a-fmcw-radar},
doi = {10.1109/IROS51168.2021.9635901},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {117–122},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Stavrow Bahnam; Sven Pfeiffer; Guido C. H. E. Croon Stereo Visual Inertial Odometry for Robots with Limited Computational Resources* (Proceedings Article) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 9154–9159, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (This work was supported by Royal Brinkman 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. ; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{6e78308d25d147c1b66c2f105ce09c1b,
title = {Stereo Visual Inertial Odometry for Robots with Limited Computational Resources*},
author = {Stavrow Bahnam and Sven Pfeiffer and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/stereo-visual-inertial-odometry-for-robots-with-limited-computati},
doi = {10.1109/IROS51168.2021.9636807},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {9154–9159},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {This work was supported by Royal Brinkman 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. ; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Bardienus P. Duisterhof; Shushuai Li; Javier Burgues; Vijay Janapa Reddi; Guido C. H. E. Croon Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments (Proceedings Article) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, pp. 9099–9106, IEEE, United States, 2021, ISBN: 978-1-6654-1715-0, (2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021). @inproceedings{c3228644f1df4281a73cef1bf5fc35fb,
title = {Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments},
author = {Bardienus P. Duisterhof and Shushuai Li and Javier Burgues and Vijay Janapa Reddi and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/sniffy-bug-a-fully-autonomous-swarm-of-gas-seeking-nano-quadcopte},
doi = {10.1109/IROS51168.2021.9636217},
isbn = {978-1-6654-1715-0},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021},
pages = {9099–9106},
publisher = {IEEE},
address = {United States},
series = {IEEE International Conference on Intelligent Robots and Systems},
note = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ; Conference date: 27-09-2021 Through 01-10-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Federico Paredes-Vallés; Guido C. H. E. Croon Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy (Proceedings Article) In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3445–3454, IEEE, United States, 2021, ISBN: 978-1-6654-4510-8, (2021 IEEE/CVF Conference on Computer Vision<br/>and Pattern Recognition, CVPR 2021 ; Conference date: 20-06-2021 Through 25-06-2021). @inproceedings{e5319318c7074937b49de4d2f52f6607,
title = {Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy},
author = {Federico Paredes-Vallés and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/back-to-event-basics-self-supervised-learning-of-image-reconstruc},
doi = {10.1109/CVPR46437.2021.00345},
isbn = {978-1-6654-4510-8},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {3445–3454},
publisher = {IEEE},
address = {United States},
series = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
note = {2021 IEEE/CVF Conference on Computer Vision<br/>and Pattern Recognition, CVPR 2021 ; Conference date: 20-06-2021 Through 25-06-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Yingfu Xu; Guido C. H. E. Croon CNN-based Ego-Motion Estimation for Fast MAV Maneuvers (Proceedings Article) In: 2021 IEEE International Conference on Robotics and Automation, ICRA 2021, pp. 7606–7612, IEEE, United States, 2021, ISBN: 978-1-7281-9078-5, (ICRA 2021 : IEEE International Conference on Robotics and Automation ; Conference date: 30-05-2021 Through 05-06-2021). @inproceedings{8ae714c4290b4bd7a3a8f1ca78774fdd,
title = {CNN-based Ego-Motion Estimation for Fast MAV Maneuvers},
author = {Yingfu Xu and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/cnn-based-ego-motion-estimation-for-fast-mav-maneuvers},
doi = {10.1109/ICRA48506.2021.9561714},
isbn = {978-1-7281-9078-5},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE International Conference on Robotics and Automation, ICRA 2021},
pages = {7606–7612},
publisher = {IEEE},
address = {United States},
series = {Proceedings - IEEE International Conference on Robotics and Automation},
note = {ICRA 2021 : IEEE International Conference on Robotics and Automation ; Conference date: 30-05-2021 Through 05-06-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Bardienus P. Duisterhof; Srivatsan Krishnan; Jonathan J. Cruz; Colby R. Banbury; William Fu; Aleksandra Faust; Guido C. H. E. Croon; Vijay Janapa Reddi Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter (Proceedings Article) In: 2021 IEEE International Conference on Robotics and Automation, ICRA 2021, pp. 7242–7248, IEEE, United States, 2021, ISBN: 978-1-7281-9078-5, (ICRA 2021 : IEEE International Conference on Robotics and Automation ; Conference date: 30-05-2021 Through 05-06-2021). @inproceedings{9c19e80e87b24796823e537a571a3b10,
title = {Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter},
author = {Bardienus P. Duisterhof and Srivatsan Krishnan and Jonathan J. Cruz and Colby R. Banbury and William Fu and Aleksandra Faust and Guido C. H. E. Croon and Vijay Janapa Reddi},
url = {https://research.tudelft.nl/en/publications/tiny-robot-learning-tinyrl-for-source-seeking-on-a-nano-quadcopte},
doi = {10.1109/ICRA48506.2021.9561590},
isbn = {978-1-7281-9078-5},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE International Conference on Robotics and Automation, ICRA 2021},
pages = {7242–7248},
publisher = {IEEE},
address = {United States},
series = {Proceedings - IEEE International Conference on Robotics and Automation},
note = {ICRA 2021 : IEEE International Conference on Robotics and Automation ; Conference date: 30-05-2021 Through 05-06-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Julien Dupeyroux; Jesse J. Hagenaars; Federico Paredes-Vallés; Guido C. H. E. Croon Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor (Proceedings Article) In: 2021 IEEE International Conference on Robotics and Automation, ICRA 2021, pp. 96–102, IEEE, United States, 2021, ISBN: 978-1-7281-9078-5, (ICRA 2021 : IEEE International Conference on Robotics and Automation ; Conference date: 30-05-2021 Through 05-06-2021). @inproceedings{f3602a9cc14d43009571062d2481863a,
title = {Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor},
author = {Julien Dupeyroux and Jesse J. Hagenaars and Federico Paredes-Vallés and Guido C. H. E. Croon},
url = {https://research.tudelft.nl/en/publications/neuromorphic-control-for-optic-flow-based-landing-of-mavs-using-t},
doi = {10.1109/ICRA48506.2021.9560937},
isbn = {978-1-7281-9078-5},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE International Conference on Robotics and Automation, ICRA 2021},
pages = {96–102},
publisher = {IEEE},
address = {United States},
series = {Proceedings - IEEE International Conference on Robotics and Automation},
note = {ICRA 2021 : IEEE International Conference on Robotics and Automation ; Conference date: 30-05-2021 Through 05-06-2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Masters Theses
|
Bas Beurden Scalable Positioning Method for MAV Localisation using Two onboard UWB Tags (Masters Thesis) TU Delft Aerospace Engineering, 2021, (Pfeiffer, S.U. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:bb6ace70-512e-4e0e-a834-6b065ece52c5,
title = {Scalable Positioning Method for MAV Localisation using Two onboard UWB Tags},
author = {Bas Beurden},
url = {http://resolver.tudelft.nl/uuid:bb6ace70-512e-4e0e-a834-6b065ece52c5},
year = {2021},
date = {2021-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Abstract—Ultra-wideband (UWB) ranging is a very suitable method for indoor localisation of unmanned aerial vehicles (UAVs). Current solutions of UWB ranging however either focus on achieving a high accuracy or focus on scalability. In this research a positioning algorithm for UAVs is presented that combines high accuracy performance with a high level of system scalability. The localisation method uses commercially available off the shelf components and is implemented by connecting two UWB sensors to a micro aerial vehicle. From<br/>both sensors, time-difference of arrival (TDOA) measurements were collected during flights and additionally, a tag-TDOA between the two UWB sensors was measured which estimates the angle-of-arrival of the incoming signals. It was found that state estimation using TDOA measurements from both UWB sensors has a reduced positioning error compared to the algorithm using TDOA measurements from one UWB sensor, without significantly affecting yaw estimation accuracy. Furthermore, the tag-TDOA measurement did not improve the estimation accuracy at the implemented baseline of 0.22 metres as the<br/>measurement error was too large compared to the baseline.},
note = {Pfeiffer, S.U. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Abstract—Ultra-wideband (UWB) ranging is a very suitable method for indoor localisation of unmanned aerial vehicles (UAVs). Current solutions of UWB ranging however either focus on achieving a high accuracy or focus on scalability. In this research a positioning algorithm for UAVs is presented that combines high accuracy performance with a high level of system scalability. The localisation method uses commercially available off the shelf components and is implemented by connecting two UWB sensors to a micro aerial vehicle. From<br/>both sensors, time-difference of arrival (TDOA) measurements were collected during flights and additionally, a tag-TDOA between the two UWB sensors was measured which estimates the angle-of-arrival of the incoming signals. It was found that state estimation using TDOA measurements from both UWB sensors has a reduced positioning error compared to the algorithm using TDOA measurements from one UWB sensor, without significantly affecting yaw estimation accuracy. Furthermore, the tag-TDOA measurement did not improve the estimation accuracy at the implemented baseline of 0.22 metres as the<br/>measurement error was too large compared to the baseline. |
Marina Gonzalez Alvarez Evolved Neuromorphic Altitude Controller for an Autonomous Blimp (Masters Thesis) TU Delft Aerospace Engineering, 2021, (Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Corradi, Federico (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9,
title = {Evolved Neuromorphic Altitude Controller for an Autonomous Blimp},
author = {Marina Gonzalez Alvarez},
url = {http://resolver.tudelft.nl/uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9},
year = {2021},
date = {2021-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III.},
note = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Corradi, Federico (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III. |
Sunyi Wang Thermistor-based airflow sensing on a flapping wing micro air vehicle (Masters Thesis) TU Delft Aerospace Engineering, 2021, (van Oudheusden, B.W. (mentor); de Croon, G.C.H.E. (graduation committee); Olejnik, D.A. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:0f908624-ddf3-4329-817e-3170d2b6b656,
title = {Thermistor-based airflow sensing on a flapping wing micro air vehicle},
author = {Sunyi Wang},
url = {http://resolver.tudelft.nl/uuid:0f908624-ddf3-4329-817e-3170d2b6b656},
year = {2021},
date = {2021-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Flow sensing exists widely in nature to help animals perform certain tasks. It has also been widely adopted in engineering applications with different types of sensing instrumentation. In particular, in the field of aerospace engineering, airflow sensing is crucial to vehicle state evaluation and flight control. This project surveys the key mechanisms from biological features in nature that enable flow sensing and expands towards the application motivation to identify a suitable airflow sensor that can be equipped to a flapping wing micro air vehicle (FWMAV) for onboard airflow sensing. <br/><br/>The selection of sensors is first narrowed down to three major types of airflow sensors from the state of art that have the most potential to be integrated onboard a flapping wing MAV, considering the sensor performance need, size, weight and power (SWaP) restrictions. Two thermal-based commercially available low-cost airflow sensors RevP and RevC from Modern Device have been selected after the trade-off analysis. <br/><br/>A full workflow of calibrating and evaluating the two airflow sensors' directional sensitivity has been carried out through two wind tunnel campaigns. Their performance under grid-generated turbulence is compared with a constant temperature hot-wire anemometer. This series of tests leads to the conclusion that the RevP airflow sensor has better performance and is therefore chosen to be placed onboard a flapping wing MAV Delfly Nimble. <br/><br/>Both mounted tests and tethered hovering tests with the Delfly Nimble are performed to further examine the airflow sensor RevP's measurement performance under different influence factors such as MAV throttle levels, MAV body pitch angles and freestream speeds. In the end, it is concluded that as a proof of concept, the RevP sensor is capable of performing effective measurements for low flow speeds less than 4 m/s, within the pitching angle range of -30 to 30 degrees. Although this is the first achieved tethered hover flight with onboard airflow sensing for a flapping wing MAV, its limited payload and onboard power supply demands an even smaller and less power consuming design of airflow sensors to enable further applications such as autonomous reactive control under wind disturbances.},
note = {van Oudheusden, B.W. (mentor); de Croon, G.C.H.E. (graduation committee); Olejnik, D.A. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Flow sensing exists widely in nature to help animals perform certain tasks. It has also been widely adopted in engineering applications with different types of sensing instrumentation. In particular, in the field of aerospace engineering, airflow sensing is crucial to vehicle state evaluation and flight control. This project surveys the key mechanisms from biological features in nature that enable flow sensing and expands towards the application motivation to identify a suitable airflow sensor that can be equipped to a flapping wing micro air vehicle (FWMAV) for onboard airflow sensing. <br/><br/>The selection of sensors is first narrowed down to three major types of airflow sensors from the state of art that have the most potential to be integrated onboard a flapping wing MAV, considering the sensor performance need, size, weight and power (SWaP) restrictions. Two thermal-based commercially available low-cost airflow sensors RevP and RevC from Modern Device have been selected after the trade-off analysis. <br/><br/>A full workflow of calibrating and evaluating the two airflow sensors' directional sensitivity has been carried out through two wind tunnel campaigns. Their performance under grid-generated turbulence is compared with a constant temperature hot-wire anemometer. This series of tests leads to the conclusion that the RevP airflow sensor has better performance and is therefore chosen to be placed onboard a flapping wing MAV Delfly Nimble. <br/><br/>Both mounted tests and tethered hovering tests with the Delfly Nimble are performed to further examine the airflow sensor RevP's measurement performance under different influence factors such as MAV throttle levels, MAV body pitch angles and freestream speeds. In the end, it is concluded that as a proof of concept, the RevP sensor is capable of performing effective measurements for low flow speeds less than 4 m/s, within the pitching angle range of -30 to 30 degrees. Although this is the first achieved tethered hover flight with onboard airflow sensing for a flapping wing MAV, its limited payload and onboard power supply demands an even smaller and less power consuming design of airflow sensors to enable further applications such as autonomous reactive control under wind disturbances. |
Zhouxin Ge End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options (Masters Thesis) TU Delft Aerospace Engineering, 2021, (van Kampen, E. (mentor); de Croon, G.C.H.E. (graduation committee); Mitici, M.A. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:d3baec43-71d4-4f7f-ae27-2fdfdae7fea3,
title = {End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options},
author = {Zhouxin Ge},
url = {http://resolver.tudelft.nl/uuid:d3baec43-71d4-4f7f-ae27-2fdfdae7fea3},
year = {2021},
date = {2021-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy Option Critic (PPOC) is an end-to-end hierarchical reinforcement learning method that alleviates the need for a high-fidelity flight model and allows for adaptive flight control. This research contributes to the development and analysis of online adaptive flight control by comparing PPOC against a non-hierarchical method called Proximal Policy Optimization (PPO) and PPOC with a single Option (PPOC-1). The methods are tested on an extendable mass-spring-damper system and aircraft model. Subsequently, the agents are evaluated by their sample efficiency, reference tracking capability and adaptivity. The results show, unexpectedly, that PPO and PPOC-1 are more sample efficient than PPOC. Furthermore, both PPOC agents are able to successfully track the height profile, though the agents learn a policy that results in noisy actuator inputs. Finally, PPOC with multiple learned Options has the best adaptivity, as it is able to adapt to structural failure of the horizontal tailplane, sign change of pitch damping, and generalize to different aircraft.},
note = {van Kampen, E. (mentor); de Croon, G.C.H.E. (graduation committee); Mitici, M.A. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy Option Critic (PPOC) is an end-to-end hierarchical reinforcement learning method that alleviates the need for a high-fidelity flight model and allows for adaptive flight control. This research contributes to the development and analysis of online adaptive flight control by comparing PPOC against a non-hierarchical method called Proximal Policy Optimization (PPO) and PPOC with a single Option (PPOC-1). The methods are tested on an extendable mass-spring-damper system and aircraft model. Subsequently, the agents are evaluated by their sample efficiency, reference tracking capability and adaptivity. The results show, unexpectedly, that PPO and PPOC-1 are more sample efficient than PPOC. Furthermore, both PPOC agents are able to successfully track the height profile, though the agents learn a policy that results in noisy actuator inputs. Finally, PPOC with multiple learned Options has the best adaptivity, as it is able to adapt to structural failure of the horizontal tailplane, sign change of pitch damping, and generalize to different aircraft. |
Guillermo Gonzalez Archundia Position controller for a flapping-wing drone using ultra wide band (Masters Thesis) TU Delft Aerospace Engineering, 2021, (de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:b18cead1-951b-4a21-a4bb-0ba36f1768ee,
title = {Position controller for a flapping-wing drone using ultra wide band},
author = {Guillermo Gonzalez Archundia},
url = {http://resolver.tudelft.nl/uuid:b18cead1-951b-4a21-a4bb-0ba36f1768ee},
year = {2021},
date = {2021-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {The continuous improvement and miniaturisation of elements in drones have been essential for making flapping-wing drones a reality. This thesis presents an integral approach for accurate indoor position control and estimation on flapping-wing drones. The approach considers three main aspects to enhance transient response of the drone. The first one is an experimental velocity/attitude flapping-wing model for drag compensation, obtained through system identification techniques. The second one is a voltage-dependent variable thrust model for enhancing height control. Thirdly, a characterisation of ground effects to determine the height for stable hovering. For the state estimation, an extended Kalman filter fuses UWB position measurements with IMU data. Due to the well-known multi-path effects of UWB, the Kalman filter includes an adaptive noise parameter based on height. The novel control strategy was validated with real flight tests, where position control improved by a factor of 1.5, reaching a mean absolute error of 10cm in positions in x and y, and 4.9cm for position in z.},
note = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
The continuous improvement and miniaturisation of elements in drones have been essential for making flapping-wing drones a reality. This thesis presents an integral approach for accurate indoor position control and estimation on flapping-wing drones. The approach considers three main aspects to enhance transient response of the drone. The first one is an experimental velocity/attitude flapping-wing model for drag compensation, obtained through system identification techniques. The second one is a voltage-dependent variable thrust model for enhancing height control. Thirdly, a characterisation of ground effects to determine the height for stable hovering. For the state estimation, an extended Kalman filter fuses UWB position measurements with IMU data. Due to the well-known multi-path effects of UWB, the Kalman filter includes an adaptive noise parameter based on height. The novel control strategy was validated with real flight tests, where position control improved by a factor of 1.5, reaching a mean absolute error of 10cm in positions in x and y, and 4.9cm for position in z. |
Erik Vroon Motion-based MAV Detection in GPS-denied Environments (Masters Thesis) TU Delft Aerospace Engineering, 2021, (de Croon, G.C.H.E. (mentor); Rojer, Jim (mentor); Guo, J. (graduation committee); de Visser, C.C. (graduation committee); Delft University of Technology (degree granting institution)). @mastersthesis{uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2,
title = {Motion-based MAV Detection in GPS-denied Environments},
author = {Erik Vroon},
url = {http://resolver.tudelft.nl/uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2},
year = {2021},
date = {2021-01-01},
school = {TU Delft Aerospace Engineering},
abstract = {Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input.},
note = {de Croon, G.C.H.E. (mentor); Rojer, Jim (mentor); Guo, J. (graduation committee); de Visser, C.C. (graduation committee); Delft University of Technology (degree granting institution)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input. |
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?
