Conflict-Based Search for Multi-Robot Motion Planning with Kinodynamic Constraints

Multi-robot motion planning (MRMP) is the fundamental problem of finding non-colliding trajectories for multiple robots acting in an environment, under kinodynamic constraints. Due to its complexity, existing algorithms are either incomplete, or utilize simplifying assumptions. This work introduces...

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Veröffentlicht in:Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems S. 13494 - 13499
Hauptverfasser: Kottinger, Justin, Almagor, Shaull, Lahijanian, Morteza
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 23.10.2022
Schlagworte:
Benchmark testing
Collision avoidance
Complexity theory
Intelligent robots
Planning
Probabilistic logic
Trajectory
ISSN:2153-0866
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Zusammenfassung:Multi-robot motion planning (MRMP) is the fundamental problem of finding non-colliding trajectories for multiple robots acting in an environment, under kinodynamic constraints. Due to its complexity, existing algorithms are either incomplete, or utilize simplifying assumptions. This work introduces Kinodynamic Conflict-Based Search (K-CBS), a decentralized MRMP algorithm that is general, scalable, and probabilistically complete. The algorithm takes inspiration from successful solutions to the discrete analogue of MRMP over finite graphs, known as Multi-Agent Path Finding (MAPF). Specifically, we adapt ideas from Conflict-Based Search (CBS)-a popular decentralized MAPF algorithm-to the MRMP setting. The novelty of our approach is that we work directly in the continuous domain, without discretization. In particular, the kinodynamic constraints are treated natively. K-CBS plans for each robot individually using a low-level planner and grows a conflict tree to resolve collisions between robots by defining constraints. The low-level planner can be any sampling-based, tree-search algorithm for kinodynamic robots, thus lifting existing planners for single robots to the multi-robot setting. We show that K-CBS inherits the (probabilistic) completeness of the low-level planner. We illustrate the generality and performance of K-CBS in several case studies and benchmarks.
ISSN:2153-0866
DOI:10.1109/IROS47612.2022.9982018