Collision-free Trajectory Planning for Autonomous Surface Vehicle

Wen, Licheng; Yan, Jiaqing; Yang, Xin; Liu, Yong; Gu, Yong

doi:10.1109/aim43001.2020.9158907

Cited by 4 publications

(1 citation statement)

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“…Multi-Agent Path Finding (MAPF) is the problem of moving agents from their start positions to goal positions while avoiding collisions with other agents. MAPF can be used in many robotic systems, such as automatic forklifts, automatic parking systems, unmanned vehicles [1], drones, and some underwater robots.…”

Section: Introductionmentioning

confidence: 99%

Enhanced priority-based search for multi-agent path finding in roadway map

Chen¹,

Lü²,

Li³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Previous Multi-Agent Path Finding (MAPF) solvers rely on several simplifying assumptions. They consider the agents as holonomic robots and ignore the agent's size. There are two main directions of MAPF research. One is about MAPF's own shortcoming, how to improve the efficiency of the existing algorithm and the quality of the solution. The other direction is how to deal with diverse constraints brought by different cases when applying MAPF to practical problems. In this paper, the latter direction for solving the handling problem of forklifts in the roadway is more focused on. Firstly, we define MAPF for forklift robot which considers the agent's size and the topological paths as vertices. Secondly, we exploit the advantages of ECBS to make PBS runs faster with a bounded suboptimal solution. Then, we propose an acceleration strategy for collision detection, which is an important factor limiting the speed of the entire algorithm. Finally, we use the weighted low-level search to speed up calculation. The above series of improvements have enabled the MAPF algorithm to be applied in our real scenarios.

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Section: Introductionmentioning

confidence: 99%