Autonomous Navigation and Obstacle Avoidance for Mobile Robots in a Tax Environment

This paper focuses on the navigation and obstacle avoidance problems of intelligent mobile robots in a tax environment. Through combining the improved jump point search (JPS) and dynamic window approach (DWA) algorithms, a fusion path planning method is proposed for the mobile robots in tax services. Specially, a bi-directional JPS algorithm with an adaptive heuristic function is provided to decrease the search complexity and improve search flexibility in the global path planning. Meanwhile, considering the features of the tax environment, several evaluation subfunctions are particularly designed to leverage the global information and optimize the local path planning. Finally, various simulations and experiments in a Turtlebot3 robot validate the feasibility and effectiveness of the proposed method. The obtained results provide valuable guidance for the autonomous navigation research of the tax service robots.