Optimizing obstacle avoidance path planning for intelligent mobile robots in multi-obstacle environments

To enhance the obstacle avoidance capabilities of intelligent mobile robots in environments with multiple obstacles, a method for optimal path planning has been developed and analysed. Multi-obstacle scene maps were generated using the safety boundary condition method and envelope method, with obstacle distribution abstractly represented by an obstacle rate. The robot's pose and orientation were determined through the application of Adaptive Monte Carlo localization. Speed sampling was conducted to identify the optimal speed combination, establishing a set of candidate trajectories. A trajectory evaluation function was constructed, and the trajectory with the highest score was selected. The improved A* algorithm was integrated into the evaluation function, incorporating the obstacle rate to refine path planning. To eliminate redundant sections, a key point extraction algorithm was employed to adjust and smooth the path, ensuring optimal obstacle avoidance. The proposed approach demonstrated superior performance, achieving a shorter planned path, a rapid obstacle avoidance reaction time of 32 milliseconds, a 100 % success rate in obstacle avoidance, and a path smoothness of 97 %. These results indicate that the method is highly effective for obstacle avoidance, with significant improvements in both safety and efficiency.