Publications - MAVLab

In our recent Nature article, we developed a new theory on how flying drones and insects can estimate gravity direction by combining visual motion sensing with a model of how they move. This discovery forms a parsimonious explanation of multiple phenomena observed in biology and is also an important step for the creation of autonomous tiny drones with even fewer sensors.

Guido C. H. E. Croon, Julien J. G. Dupeyroux, Christophe De Wagter, Abhishek Chatterjee, Diana A. Olejnik, Franck Ruffier: Accommodating unobservability to control flight attitude with optic flow. In: Nature, vol. 610, no. 7932, pp. 485–490, 2022, ISSN: 0028-0836.

Nature Machine Intelligence Paper

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?

The article is featured on the cover of Nature Machine Intelligence. Cover reprinted by permission from Springer Nature.

G.C.H.E. de Croon, C. De Wagter, T. Seidl: Enhancing optical-flow-based control by learning visual appearance cues for flying robots. In: Nature Machine Intelligence, vol. 3, no. 1, 2021.

Science Robotics Paper

Our recent paper in Science Robotics presents the first swarm of tiny robots that can explore unknown environments completely by themselves. No map, no memory, and no GPS! The full paper can be found here.

K N McGuire, C De Wagter, K Tuyls, H J Kappen, G C H E de Croon: Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment. In: Science Robotics, vol. 4, no. 35, 2019.

Delfly Book

Our book about the DelFly has been published! The book introduces the topics most relevant to autonomously flying flapping-wing robots: flapping-wing design, aerodynamics, and artificial intelligence.

G C H E de Croon, Mustafa Percin, B D W Remes, Rick Ruijsink, C De Wagter: The DelFly - Design, Aerodynamics, and Artificial Intelligence of a Flapping Wing Robot. Springer Netherlands, 2016, ISBN: 978-94-017-9207-3.

Science paper

The Delfly Nimble was featured on the cover of Science Magazine in September 2018.

Matěj Karásek, Florian T Muijres, Christophe De Wagter, Bart D W Remes, Guido C H E de Croon: A tailless aerial robotic flapper reveals that flies use torque coupling in rapid banked turns. In: Science, vol. 361, no. 6407, pp. 1089–1094, 2018, ISSN: 0036-8075.

All publications

679 entries « ‹ 9 of 14 › »

2018

Masters Theses

Shubham Vyas

Uncertainty Estimation in Vision-Aided Robot Teleoperation System (Masters Thesis)

TU Delft Aerospace Engineering; TU Delft Space Engineering, 2018, (Verhoeven, C.J.M. (mentor); Krueger, Thomas (mentor); Schiele, A. (mentor); Gill, E.K.A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)).

(Abstract | Links | BibTeX)

@mastersthesis{uuid:4a03785d-8822-4548-a24a-7b23b1a4232c,

title = {Uncertainty Estimation in Vision-Aided Robot Teleoperation System},

author = {Shubham Vyas},

url = {http://resolver.tudelft.nl/uuid:4a03785d-8822-4548-a24a-7b23b1a4232c},

year  = {2018},

date = {2018-01-01},

school = {TU Delft Aerospace Engineering; TU Delft Space Engineering},

abstract = {Teleoperation allows the use of human intelligence and decision making in remote tasks which are too dangerous for humans to perform. Technologies such as force feedback and haptic guidance have shown to increase task efficiency during teleoperation. In an unmodeled environment, sensors provide input for haptic guidance or present extra information about the environment to the user in order to make decisions and to perform the tasks. These sensors come with inherent errors and uncertainties which propagate through the teleoperation system. The absence of knowledge of these errors has been shown to cause deterioration in the task performance. These errors can further cause the application of forces on the environment by the robot without the knowledge of the user while using haptic guidance. Thereby, the strategies being used to increase task performance can have some adverse hidden effects. Thus, it crucial to have an understanding of the behaviour of the errors and uncertainties in the system. It is considered critical for making decisions about how the robot system can be controlled and used to manipulate objects remotely.<br/><br/>In this thesis, a novel framework for estimating the uncertainties in a vision-aided teleoperation system in real-time is introduced. The uncertainty estimate can then be used by the control system or communicated to the user. Methods to use the uncertainty estimate for haptic guidance and for user display are proposed. Furthermore, the thesis analyzes the behaviour of the uncertainties in the system and the sensitivity of the system to individual component errors. It evaluates the uncertainties in individual components of the system and implements an uncertainty model for each of them. It then provides a method to propagate these uncertainty models through the system. This results in a final uncertainty estimate in the frame of reference of interest for the task. Experiments were performed to validate the component uncertainty models, the propagation method, and the system as a whole. Additionally, an inverse of the propagation method is also conceived so as to obtain the component accuracy specification from system uncertainty requirements. This can be used in the design of future teleoperation systems.},

note = {Verhoeven, C.J.M. (mentor); Krueger, Thomas (mentor); Schiele, A. (mentor); Gill, E.K.A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},

keywords = {},

pubstate = {published},

tppubtype = {mastersthesis}

}

Teleoperation allows the use of human intelligence and decision making in remote tasks which are too dangerous for humans to perform. Technologies such as force feedback and haptic guidance have shown to increase task efficiency during teleoperation. In an unmodeled environment, sensors provide input for haptic guidance or present extra information about the environment to the user in order to make decisions and to perform the tasks. These sensors come with inherent errors and uncertainties which propagate through the teleoperation system. The absence of knowledge of these errors has been shown to cause deterioration in the task performance. These errors can further cause the application of forces on the environment by the robot without the knowledge of the user while using haptic guidance. Thereby, the strategies being used to increase task performance can have some adverse hidden effects. Thus, it crucial to have an understanding of the behaviour of the errors and uncertainties in the system. It is considered critical for making decisions about how the robot system can be controlled and used to manipulate objects remotely.<br/><br/>In this thesis, a novel framework for estimating the uncertainties in a vision-aided teleoperation system in real-time is introduced. The uncertainty estimate can then be used by the control system or communicated to the user. Methods to use the uncertainty estimate for haptic guidance and for user display are proposed. Furthermore, the thesis analyzes the behaviour of the uncertainties in the system and the sensitivity of the system to individual component errors. It evaluates the uncertainties in individual components of the system and implements an uncertainty model for each of them. It then provides a method to propagate these uncertainty models through the system. This results in a final uncertainty estimate in the frame of reference of interest for the task. Experiments were performed to validate the component uncertainty models, the propagation method, and the system as a whole. Additionally, an inverse of the propagation method is also conceived so as to obtain the component accuracy specification from system uncertainty requirements. This can be used in the design of future teleoperation systems.

Fede Paredes Valles

Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation (Masters Thesis)

TU Delft Aerospace Engineering; TU Delft Control & Simulation, 2018, (de Croon, G.C.H.E. (mentor); Scheper, K.Y.W. (mentor); Delft University of Technology (degree granting institution)).

(Abstract | Links | BibTeX)

Miscellaneous

Matej Karasek

Flapping wing aerial vehicle (Miscellaneous)

2018, (A63H; B64C).

Nature Machine Intelligence Paper

Science Robotics Paper

Delfly Book

Science paper

All publications

2018

Masters Theses

Miscellaneous

PhD Theses

2017

Journal Articles

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Proceedings Articles