Collaborative Design of Path Planning and Motion Control for Mobile Robots in Complex Environments
Ming Qian · Automation and Machine Learning · 2025
Given how quickly mobile robot technology is developing, utilizing current path planning techniques in complex environments faces challenges in dynamic obstacle avoidance, path optimization and real-time response. To deal with these problems, this paper proposes a collaborative design method for mobile robot path planning and motion control for complex environments. This paper introduces an improved version of the RRT* (Rapidly Expanding Random Tree) algorithm, combined with adaptive path adjustment and dynamic obstacle avoidance strategies, to help increase the robot's movements accuracy and planning of paths efficiency in challenging situations. The RRT* algorithm can efficiently create a workable route between the beginning and destination points while minimizing the path length and considering motion constraints. The adaptive path adjustment method updates the path in real time during the robot operation, especially under the influence of dynamic obstacles, to guarantee the path's stability and consistency. The motion control system works in conjunction with the path planning algorithm to ensure that the robot accurately tracks the optimized path and maintains safe driving. Through detailed simulation experiments, this paper compares the performance of this method with traditional algorithms (A* algorithm and basic RRT algorithm), focusing on evaluating key indicators such as execution time, path length, and obstacle avoidance performance. The experimental results show that in terms of collaborative effect, the improved RRT scores 8.5 in a static environment, higher than A*'s 7.8, indicating that its path planning and motion control collaborative optimization effect is better.