Autonomous Path Planning for Mobile Robots in Multi Obstacle Environments

The mobile robot will suffer from a lack of direction in the search, numerous redundant nodes, and a prolonged algorithm running time when there are more obstacles in the environment. As a result, the attraction force of the goal point to the robot and the repulsive force of the obstacle to the mobile robot are utilized in conjunction to identify the growth direction of the growing tree in this study, which is based on RRT and the operating principle of the artificial potential field technique. The simulation results indicate that in a multi obstacle environment, the enhanced RRT algorithm outperformed the conventional RRT algorithm in terms of autonomous path planning efficiency by 6.15 times, reduced the length of the planned path by 34.24 meters, and decreased the average number of iterations by 46.93%; And the upgraded RRT algorithm has 2.23 times the efficiency in autonomous path planning compared to the RRT* algorithm and 14.50% fewer iterations on average, but its projected ideal path is 0.61 meters longer. Therefore, the improved RRT algorithm has more directional search, more efficient planning efficiency, and relatively fewer average iterations when conducting path planning.