Energy-Efficient Multi-Robot Coverage Path Planning of Non-Convex Regions of Interests

Researchers from Florida International University and collaborating institutions have introduced MRCPP (Multi-Robot Coverage Path Planning), a novel framework designed to optimize energy efficiency for robot teams covering large, non-convex areas with obstacles and no-fly zones. Unlike existing meta-heuristic boustrophedon decomposition methods, MRCPP generates globally-informed swath patterns that create parallel sweeping paths with minimal turns, significantly reducing energy waste. It also calculates safety buffers for turning clearance and uses an efficient mTSP solver to balance workloads and minimize mission time. Disjoint path segments are connected via a modified visibility graph that tracks heading angles while ensuring transitions remain within safe regions.

Real-world experiments with autonomous aerial vehicles (AAVs) and autonomous surface vehicles (ASVs) demonstrated MRCPP's superiority over state-of-the-art planners. For a team of 3 robots, it reduced average total energy consumption by 3% to 40% and cut computation time by an order of magnitude, all while maintaining balanced workload distribution and strong scalability as fleet sizes increased. The framework is released as an open-source package, with videos of experiments available online. This work, accepted in IEEE Robotics and Automation Letters, addresses key limitations in current coverage planning for heterogeneous robot teams operating in complex environments.