MSTC∗:Multi-robot Coverage Path Planning under Physical Constrain
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021•ieeexplore.ieee.org
For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots.
In this paper, we present an efficient algorithm MSTC∗ for multi-robot coverage path
planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm
incorporates strict physical constraints like terrain traversability and material load capacity.
We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and
real field terrains in simulation environments. The experimental results show that our method …
In this paper, we present an efficient algorithm MSTC∗ for multi-robot coverage path
planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm
incorporates strict physical constraints like terrain traversability and material load capacity.
We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and
real field terrains in simulation environments. The experimental results show that our method …
For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC ∗ for multi-robot coverage path planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm incorporates strict physical constraints like terrain traversability and material load capacity. We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and real field terrains in simulation environments. The experimental results show that our method significantly outperforms existing spiral-STC based mCPP methods. Our algorithm can find a set of well-balanced workload distributions for all robots and therefore, achieve the overall minimum time to complete the coverage.
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