C*: A Coverage Path Planning Algorithm for Unknown Environments using Rapidly Covering Graphs
Zongyuan Shen, James P. Wilson, Shalabh Gupta
TL;DR
This work addresses CPP in unknown environments by introducing $C^*$, a sample-based algorithm built on progressively constructed Rapidly Covering Graphs to guarantee complete coverage. It combines dead-end escape and proactive coverage hole prevention via local TSP optimization to reduce overlaps and trajectory length, while maintaining real-time feasibility. The approach is validated through high-fidelity simulations and real lab experiments, showing significant improvements over seven baselines and near-optimal path lengths. Extensions to energy-constrained and multi-robot CPP demonstrate the method's practical impact for diverse robotic applications.
Abstract
The paper presents a novel sample-based algorithm, called C*, for real-time coverage path planning (CPP) of unknown environments. C* is built upon the concept of a Rapidly Covering Graph (RCG), which is incrementally constructed during robot navigation via progressive sampling of the search space. By using efficient sampling and pruning techniques, the RCG is constructed to be a minimum-sufficient graph, where its nodes and edges form the potential waypoints and segments of the coverage trajectory, respectively. The RCG tracks the coverage progress, generates the coverage trajectory and helps the robot to escape from the dead-end situations. To minimize coverage time, C* produces the desired back-and-forth coverage pattern, while adapting to the TSP-based optimal coverage of local isolated regions, called coverage holes, which are surrounded by obstacles and covered regions. It is analytically proven that C* provides complete coverage of unknown environments. The algorithmic simplicity and low computational complexity of C* make it easy to implement and suitable for real-time on-board applications. The performance of C* is validated by 1) extensive high-fidelity simulations and 2) laboratory experiments using an autonomous robot. C* yields near optimal trajectories, and a comparative evaluation with seven existing CPP methods demonstrates significant improvements in performance in terms of coverage time, number of turns, trajectory length, and overlap ratio, while preventing the formation of coverage holes. Finally, C* is comparatively evaluated on two different CPP applications using 1) energy-constrained robots and 2) multi-robot teams.