融合引导搜索与渐次优化的双轴铰接式车辆狭窄空间运动规划方法

Articulated autonomous vehicle (AAV) consists of a powered tractor and several unpow- ered trailers. When the AAV maneuvers in narrow space, the increment of trailer amount leads to an increase in the scalc of the collision avoidance constraints, and rcduccs the real-time capability of the motion planning tasks. To address this, an AAV motion planning method that combines guided search and progressive optimization is proposed. Firstly, the general kinematic model and planning task constraints for a two-axle AAV arc derived. Secondly, the classical hybrid A∗ algorithm is improved by considering the characteristics of the environment and the AAV behaviour, and the initial path that matches the characteristics of the AAV motion is generated by guiding the search direction towards the target node. Finally, the interior point method (IPM) is used for the optimization, with the initial path used as the reference solution for the warm start. After shrinking the obstacle size, it is gradually expanded to the original size, and a series of collision avoidance constraint subproblems are constructed to speed up the solving process. Simulation experiment results show that the guided search algorithm can improve the planning efficiency by 40.% ∼ 50 % when towing different amount of trailers, and the smoothness of the generated path is better. The progressive optimization algorithm can improve the path quality by mitigating the small amount of backward maneuver and sudden changes of heading angle in the initial solution generated by the guided search algorithm.