Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1017-1022 |
| Seitenumfang | 6 |
| Fachzeitschrift | IFAC-PapersOnLine |
| Jahrgang | 56 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 23 Nov. 2023 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 22nd IFAC World Congress - Yokohama, Japan Dauer: 9 Juli 2023 → 14 Juli 2023 |
Abstract
In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
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in: IFAC-PapersOnLine, Jahrgang 56, Nr. 2, 2023, S. 1017-1022.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Informed Circular Fields for Global Reactive Obstacle Avoidance of Robotic Manipulators
AU - Becker, Marvin
AU - Caspers, Philipp
AU - Hattendorf, Tom
AU - Lilge, Torsten
AU - Haddadin, Sami
AU - Müller, Matthias A.
N1 - Publisher Copyright: Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
PY - 2023
Y1 - 2023
N2 - In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.
AB - In this paper a global reactive motion planning framework for robotic manipulators in complex dynamic environments is presented. In particular, the circular field predictions (CFP) planner from Becker et al. (2021) is extended to ensure obstacle avoidance of the whole structure of a robotic manipulator. Towards this end, a motion planning framework is developed that leverages global information about promising avoidance directions from arbitrary configuration space motion planners, resulting in improved global trajectories while reactively avoiding dynamic obstacles and decreasing the required computational power. The resulting motion planning framework is tested in multiple simulations with complex and dynamic obstacles and demonstrates great potential compared to existing motion planning approaches.
KW - Autonomous robotic systems
KW - Guidance navigation and control
KW - Motion Planning
KW - Real-Time Collision Avoidance
KW - Robots manipulators
UR - http://www.scopus.com/inward/record.url?scp=85181631254&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2023.10.1698
DO - 10.1016/j.ifacol.2023.10.1698
M3 - Conference article
VL - 56
SP - 1017
EP - 1022
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 2
T2 - 22nd IFAC World Congress
Y2 - 9 July 2023 through 14 July 2023
ER -