Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments

Abstract

Coverage path planning (CPP) of multiple Dubins robots has been extensively applied in aerial monitoring, marine exploration, and search and rescue. Existing multi-robot coverage path planning (MCPP) research use exact or heuristic algorithms to address coverage applications. However, several exact algorithms always provide precise area division rather than coverage paths, and heuristic methods face the challenge of balancing accuracy and complexity. This paper focuses on the Dubins MCPP problem of known environments. Firstly, we present an exact Dubins multi-robot coverage path planning (EDM) algorithm based on mixed linear integer programming (MILP). The EDM algorithm searches the entire solution space to obtain the shortest Dubins coverage path. Secondly, a heuristic approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm is presented, which utilizes the credit model to balance tasks among robots and a tree partition strategy to reduce complexity. Comparison experiments with other exact and approximate algorithms demonstrate that EDM provides the least coverage time in small scenes, and CDM produces a shorter coverage time and less computation time in large scenes. Feasibility experiments demonstrate the applicability of EDM and CDM to a high-fidelity fixed-wing unmanned aerial vehicle (UAV) model.