In order to solve the problems of low efficiency of path planning in complex environments, disconnection between local obstacle avoidance and trajectory execution, and easy amplification of tracking error in dynamic disturbances, this paper proposes an integrated adaptive control method for path planning and trajectory tracking. In the planning layer, guided sampling and risk cost field modeling were introduced to improve the search efficiency, path smoothness and reachability under complex obstacle distribution. In the control layer, an adaptive tracking structure including feedforward compensation, error feedback and online gain adjustment is constructed to enhance the system’s ability to suppress model mismatch and external disturbance. The experimental results show that in the scene with 25% obstacle density, the average planning time of the proposed method is 0.43 s, the average path length is 78.4 m, and the task success rate reaches 96.7%. When the obstacle density increases to 35%, the success rate still maintains 91.5%. In the field test, the average tracking error in the dynamic obstacle scene is 0.038 m, the maximum lateral error is 0.074 m, and the replanning recovery time is 0.93 s. The results show that the proposed method achieves a good balance between path quality, control ride comfort and environmental adaptability.
