A motion planning method for a car based on environmental feature-adaptive polynomial fitting and intelligent obstacle avoidance strategies

Abstract

This paper proposes an improved method for car motion planning aimed at addressing the limitations of traditional path planning and obstacle avoidance algorithms in complex environments. The study utilizes Bi-RRT* and polynomial fitting for path planning, incorporating an environment-adaptive polynomial fitting technique based on obstacle density to enhance path precision in areas with high obstacle density. In the local planning phase, intelligent switching of the car’s obstacle avoidance strategies is implemented, allowing the car to use reverse motion or lateral avoidance in high-density regions to prevent stalling. Furthermore, problem decomposition and approximation methods are applied to large-scale quadratic programming (QP) problems in path tracking, improving the efficiency of the MPC algorithm. Experimental results demonstrate that the proposed method significantly enhances the car’s motion performance and stability in complex environments.

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