Research on path planning algorithm of inspection robot based on improved A* and dynamic window approach

Abstract

To address the current issues of path planning and limited applicability in patrol robots, an enhancement has been made to the existing A* algorithm, coupled with the utilization of the dynamic window approach, to design a global dynamic path planning method suitable for patrol robots. This approach involves introducing dynamic weights to enhance search efficiency, identifying collinearity among global path nodes to eliminate redundant points, and conducting obstacle detection between essential nodes to enhance path smoothness. An autonomous navigation algorithm is proposed, wherein the path generated by the A* algorithm is adopted as the global path. The essential nodes within this path are treated as local target points. By adjusting the initial pose angle and optimizing the weight coefficients of the evaluation function, the autonomous navigation algorithm’s optimization efficiency and obstacle avoidance capability against random obstacles are improved. Comparative simulation experiments are conducted to validate its robust performance in terms of planning efficiency, path length, and random obstacle avoidance. By establishing a ROS robot operating platform and conducting experiments in both static and dynamic environments, the autonomous navigation algorithm is shown to successfully plan superior paths and achieve real-time obstacle avoidance to reach the target point.