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About this paper
Zongyuan Shen, Shalabh Gupta, Shancheng Zhao, Dehua Zhou, Gao Wang, Zhongqiang Ren, Yaming Ou, Yikui Zhai and 1 more
A comprehensive survey systematically reviews 125 works on Coverage Path Planning (CPP), creating a problem-driven taxonomy that unifies classical methodologies with recent advancements across single-robot, multi-robot, 3D, constrained, learning-based, and visual coverage. It identifies key trends like subarea decomposition and the evolution to non-local online planning, alongside future challenges such as scalable online planning and integrated 3D visual coverage.
Key insights
- Subarea decomposition serves as a fundamental principle across many CPP settings, effectively reducing planning complexity from single-robot to 3D and multi-robot scenarios.
- Online single-robot CPP has evolved from local, myopic decision-making to non-local approaches that integrate higher-level decision structures for improved path quality and reduced fragmentation.
- Multi-robot CPP research has progressed towards subarea-level coordination, which significantly improves workload balancing and reduces inter-robot interference compared to finer-grained coordination.
Summary by alphaXiv.
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