Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning
We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restriction...
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| Published in | Journal of intelligent & robotic systems Vol. 108; no. 3; p. 50 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.07.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0921-0296 1573-0409 1573-0409 |
| DOI | 10.1007/s10846-023-01878-3 |
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| Abstract | We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restrictions (for example, autonomous vehicles may not want to share their locations or intents with other vehicles or even to a central server). The key insight that allows us to solve this problem is to stochastically distribute the robots across different routes in the environment by assigning them paths in different topologically distinct classes, so as to lower congestion and the overall travel time for all robots in the environment. We outline the computation of topologically distinct paths in a spatio-temporal configuration space and propose methods for the stochastic assignment of paths to the robots. A fast replanning algorithm and a potential field based controller allow robots to avoid collision with nearby agents while following the assigned path. Our simulation and experiment results show a significant advantage over shortest path following under such a coordination-free setup. |
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| AbstractList | We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restrictions (for example, autonomous vehicles may not want to share their locations or intents with other vehicles or even to a central server). The key insight that allows us to solve this problem is to stochastically distribute the robots across different routes in the environment by assigning them paths in different topologically distinct classes, so as to lower congestion and the overall travel time for all robots in the environment. We outline the computation of topologically distinct paths in a spatio-temporal configuration space and propose methods for the stochastic assignment of paths to the robots. A fast replanning algorithm and a potential field based controller allow robots to avoid collision with nearby agents while following the assigned path. Our simulation and experiment results show a significant advantage over shortest path following under such a coordination-free setup. |
| ArticleNumber | 50 |
| Author | Wang, Xiaolong Sahin, Alp Bhattacharya, Subhrajit |
| Author_xml | – sequence: 1 givenname: Xiaolong surname: Wang fullname: Wang, Xiaolong organization: Department of Mechanical Engineering and Mechanic, Lehigh University – sequence: 2 givenname: Alp surname: Sahin fullname: Sahin, Alp organization: Department of Mechanical Engineering and Mechanic, Lehigh University – sequence: 3 givenname: Subhrajit orcidid: 0000-0001-9139-054X surname: Bhattacharya fullname: Bhattacharya, Subhrajit email: sub216@lehigh.edu organization: Department of Mechanical Engineering and Mechanic, Lehigh University |
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| CitedBy_id | crossref_primary_10_1109_TRO_2024_3492386 crossref_primary_10_3390_machines10090773 crossref_primary_10_1016_j_birob_2024_100203 crossref_primary_10_3390_a16110498 |
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| Keywords | Topological path planning Privacy-aware planning Multi-Robot motion planning |
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| SubjectTerms | Algorithms Artificial Intelligence Collision avoidance Configuration space path planning Congestion Control Coordination Electrical Engineering Engineering Mechanical Engineering Mechatronics Multiple robots Potential fields Regular Paper Robotics Robots Topical collection on Unmanned Systems Trajectory planning Travel time |
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