APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

Aiming at the problems of the A* algorithm in mobile robot path planning, such as multiple nodes, low path accuracy, long running time and difficult path initialization of particle swarm optimization, an APSO algorithm combining A* and PSO was proposed to calculate the optimal path. First, a redundant point removal strategy is adopted to preliminarily optimize the path planned by the A* algorithm and obtain the set of key nodes. Second, a stochastic inertia weight is proposed to improve the search ability of PSO. Third, a stochastic opposition-based learning strategy is proposed to further improve the search ability of PSO. Fourth, the global path is obtained by using the improved PSO to optimize the set of key nodes. Fifth, a motion time objective function that is more in line with the actual motion requirements of the mobile robot is used to evaluate the algorithm. The simulation results of path planning show that the path planned by APSO not only reduces the running time of the mobile robot by 17.35%, 14.84%, 15.31%, 15.21%, 18.97%, 15.70% compared with the A* algorithm in the six environment maps but also outperforms other path planning algorithms to varying degrees. Therefore, the proposed APSO is more in line with the actual movement of the mobile robot.

Automated summary

This study proposes an APSO algorithm combining A* and PSO to address the issues in mobile robot path planning. The proposed algorithm optimizes the path planning by utilizing redundant point removal, stochastic inertia weight, and stochastic opposition-based learning strategies. The algorithm is evaluated using a motion time objective function that is more aligned with the actual requirements. Simulation results show that the APSO algorithm reduces the running time of the robot and outperforms other algorithms.