Priority-Aware Multi-Robot Coverage Path Planning

TL;DR

This paper introduces a new multi-robot coverage path planning approach that prioritizes certain zones, reducing weighted latency and improving efficiency in environments with non-uniform importance zones.

Contribution

The paper presents a novel Priority-Aware MCPP framework that incorporates zone priorities into path planning, combining greedy assignment, local search, and Steiner-tree methods.

Findings

Significantly reduces priority-weighted latency compared to baseline methods.

Maintains competitive overall makespan in diverse scenarios.

Scales effectively with the number of robots and adjustable priority weights.

Abstract

Multi-robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi-Robot Coverage Path Planning (MCPP), the objective is typically to minimize the makespan by generating non-overlapping paths for full-area coverage. However, most existing methods assume uniform importance across regions, limiting their effectiveness in scenarios where some zones require faster attention. We introduce the Priority-Aware MCPP (PA-MCPP) problem, where a subset of the environment is designated as prioritized zones with associated weights. The goal is to minimize, in lexicographic order, the total priority-weighted latency of zone coverage and the overall makespan. To address this, we propose a scalable two-phase framework combining (1) greedy zone assignment with local search, spanning-tree-based path planning, and (2) Steiner-tree-guided residual coverage. Experiments across diverse scenarios demonstrate that our method significantly reduces priority-weighted latency compared to standard MCPP baselines, while maintaining competitive makespan. Sensitivity analyses further show that the method scales well with the number of robots and that zone coverage behavior can be effectively controlled by adjusting priority weights.