Hybrid genetic ant colony optimization algorithm for full-coverage path planning of gardening pruning robots

Abstract

Gardening pruning robots are widely applied in green space construction. However, increase of green space environment complexity and obstacle number affect the coverage range and work efficiency of robots. To solve this problem, this research proposed a full-coverage path planning algorithm integrating hybrid genetic ant colony and A* algorithm. Specifically tailored to the lawn working environments of horticultural pruning robots, we initially employed visual simultaneous localization and mapping to create a 3D point cloud map, converting it into an occupancy grid map for future path planning. The obtained grid map was partitioned into multiple subareas on the basis of the locations of obstacles. The optimal traversal order of sub-regions was determined using hybrid genetic ant colony method and a new update strategy of heuristic and pheromone factors was developed for improving the ability of global search and probability of jumping out of local optimal solution. Boustrophedon method was applied to fully cover each sub-region, A* algorithm was adopted to connect various sub-regions, and connection strategy was optimized. Simulation results showed that compared with traditional ant colony algorithm and other full-coverage planning algorithms, the algorithm developed in this research presented superior performance in terms of traversal path length, starting distance, coverage rate and turning times on maps with various sizes and complexities.