Details
| Original language | English |
|---|---|
| Article number | 9362166 |
| Pages (from-to) | 2618-2625 |
| Number of pages | 8 |
| Journal | IEEE Robotics and Automation Letters |
| Volume | 6 |
| Issue number | 2 |
| Early online date | 24 Feb 2021 |
| Publication status | Published - Apr 2021 |
Abstract
Keywords
- Robots, Robot kinematics, Collision avoidance, Force, Planning, Robot sensing systems, Trajectory planning, Motion and Path Planning, Collision Avoidance, Reactive and Sensor-Based Planning
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Human-Computer Interaction
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Biomedical Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Robotics and Automation Letters, Vol. 6, No. 2, 9362166, 04.2021, p. 2618-2625.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Circular Fields and Predictive Multi-Agents for Online Global Trajectory Planning
AU - Becker, Marvin
AU - Lilge, T.
AU - Müller, Matthias
AU - Haddadin, S.
N1 - Funding Information: Manuscript received October 15, 2020; accepted February 6, 2021. Date of publication February 24, 2021; date of current version March 16, 2021. This work was supported in part by the Region Hannover in the project roboterfabrik and by the European Union’s Horizon 2020 Research and Innovation programme as part of the projects ILIAD under Grant 732737, in part by the Lighthouse Initiative Geriatronics by StMWi Bayern Project X, under Grant 5140951, and in part by the LongLeif GaPa gGmbH Project Y, under Grant 5140953. (Corresponding author: Marvin Becker.) Marvin Becker, Torsten Lilge, and Matthias A. Müller are with the Institute of Automatic Control, Leibniz University Hannover, Hannover 30167, Germany (e-mail: becker@irt.uni-hannover.de; lilge@irt.uni-hannover.de; mueller@irt.uni-hannover.de).
PY - 2021/4
Y1 - 2021/4
N2 - Safe and efficient trajectory planning for autonomous robots is becoming increasingly important in both industrial applications and everyday life. The demands on a robot which has to react quickly and precisely to changes in cluttered, unknown and dynamic environments are particularly high. Towards this end, based on the initial idea proposed in [27] we propose the Circular Field Predictions approach, which unifies reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planningreactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning. The proposed approach is inspired by electromagnetic fields, free of local minima and extended with artificial multi-agents to efficiently explore the environment. The algorithm is extensively analysed in complex simulation environments where it is shown to be able to generate smooth trajectories around arbitrarily shaped obstacles. Moreover, we experimentally verified the approach with a 7 Degree-of-Freedom (DoF) Franka Emika robot.
AB - Safe and efficient trajectory planning for autonomous robots is becoming increasingly important in both industrial applications and everyday life. The demands on a robot which has to react quickly and precisely to changes in cluttered, unknown and dynamic environments are particularly high. Towards this end, based on the initial idea proposed in [27] we propose the Circular Field Predictions approach, which unifies reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planningreactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning. The proposed approach is inspired by electromagnetic fields, free of local minima and extended with artificial multi-agents to efficiently explore the environment. The algorithm is extensively analysed in complex simulation environments where it is shown to be able to generate smooth trajectories around arbitrarily shaped obstacles. Moreover, we experimentally verified the approach with a 7 Degree-of-Freedom (DoF) Franka Emika robot.
KW - Robots
KW - Robot kinematics
KW - Collision avoidance
KW - Force
KW - Planning
KW - Robot sensing systems
KW - Trajectory planning
KW - Motion and Path Planning
KW - Collision Avoidance
KW - Reactive and Sensor-Based Planning
UR - http://www.scopus.com/inward/record.url?scp=85101740181&partnerID=8YFLogxK
U2 - 10.15488/11326
DO - 10.15488/11326
M3 - Article
VL - 6
SP - 2618
EP - 2625
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 2
M1 - 9362166
ER -