arXiv AI By Zongyuan Shen, Shalabh Gupta, Shancheng Zhao, Dehua Zhou, Gao Wang, Zhongqiang Ren, Yaming Ou, Yikui Zhai, C. L. Philip Chen

Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions

Read the original on arXiv AI →

arXiv:2607. 10649v1 Announce Type: cross Abstract: Coverage path planning (CPP) is a fundamental problem in robot motion planning, whose aim is to produce robot trajectories that provide complete coverage of target workspaces while minimizing task-specific objectives such as path length, overlap, number of turns, and energy consumption.

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arXiv AI
Aug 7

Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations

arXiv:2608. 05588v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones.

By He Jiang, Jingtian Yan, Yulun Zhang, Yimin Tang, Tanishq Duhan, Rishi Veerapaneni, Guillaume Sartoretti, Jiaoyang Li