NH
N. Hashimoto
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Autonomous mobile robots are increasingly performing tasks in our daily environments, e.g., cleaning offices or order picking in supermarkets.
In such human-populated scenarios, it is crucial that these robots always navigate safely when performing their task.
Existing state-of-the-art methods can ensure safety, but often require deterministic motions of humans and may lead to conservative behavior in dense human-populated environments. This study investigates the use of time-varying control barrier functions (CBFs) and time-based rapidly exploring random trees (RRTs) to combine local safety with global task performance.
The proposed new cost function improves the trade-off of safety and task progress in densely populated scenarios.
Our results show that time-varying CBFs can perform better in terms of both task performance and safety compared to normal CBFs. Furthermore, our real-world robot experiments validate our approach to a physical nonholonomic robot. ...
In such human-populated scenarios, it is crucial that these robots always navigate safely when performing their task.
Existing state-of-the-art methods can ensure safety, but often require deterministic motions of humans and may lead to conservative behavior in dense human-populated environments. This study investigates the use of time-varying control barrier functions (CBFs) and time-based rapidly exploring random trees (RRTs) to combine local safety with global task performance.
The proposed new cost function improves the trade-off of safety and task progress in densely populated scenarios.
Our results show that time-varying CBFs can perform better in terms of both task performance and safety compared to normal CBFs. Furthermore, our real-world robot experiments validate our approach to a physical nonholonomic robot. ...
Autonomous mobile robots are increasingly performing tasks in our daily environments, e.g., cleaning offices or order picking in supermarkets.
In such human-populated scenarios, it is crucial that these robots always navigate safely when performing their task.
Existing state-of-the-art methods can ensure safety, but often require deterministic motions of humans and may lead to conservative behavior in dense human-populated environments. This study investigates the use of time-varying control barrier functions (CBFs) and time-based rapidly exploring random trees (RRTs) to combine local safety with global task performance.
The proposed new cost function improves the trade-off of safety and task progress in densely populated scenarios.
Our results show that time-varying CBFs can perform better in terms of both task performance and safety compared to normal CBFs. Furthermore, our real-world robot experiments validate our approach to a physical nonholonomic robot.
In such human-populated scenarios, it is crucial that these robots always navigate safely when performing their task.
Existing state-of-the-art methods can ensure safety, but often require deterministic motions of humans and may lead to conservative behavior in dense human-populated environments. This study investigates the use of time-varying control barrier functions (CBFs) and time-based rapidly exploring random trees (RRTs) to combine local safety with global task performance.
The proposed new cost function improves the trade-off of safety and task progress in densely populated scenarios.
Our results show that time-varying CBFs can perform better in terms of both task performance and safety compared to normal CBFs. Furthermore, our real-world robot experiments validate our approach to a physical nonholonomic robot.
AETHER
The design of an electric Vertical Take Off and Landing aircraft for the transportation of passengers with reduced mobility
Bachelor thesis
(2022)
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A.E. Garcia Bulder, J. Tegischer, K. Iwamida, L. Domenech Garrido, N. Hashimoto, V. Zygouris, Y. Cürgül, J.A.P. Leijtens, M. Rehbein, J.A. Melkert, M.D. Pavel, N. Barfknecht, S. Liu