Double Heuristic Optimization Based on Hierarchical Partitioning for Coverage Path Planning of Robot Mowers

Abstract

Coverage path planning is one of the important issues for robot mowers, which brings great convenience to our life. In this paper, hierarchical partitioning strategy is employed to fulfill robot's environment modeling and the double heuristic optimization algorithms have been adopted to plan optimal coverage paths. Ant Colony Optimization (ACO) is used for global path planning in the upper layer, and Tabu Search (TS) is used for local coverage planning in the lower layer. Finally, simulation experiments are carried out and the results show the proposed method obtains satisfactory coverage path planning.