改进 Hybrid A*算法的履带车辆路径规划

The Hybrid A* algorithm was utilized to solve the problem that the paths planned by 2D grid maps are not applicable to tracked vehicles in unstructured road scenarios. An adaptive step size and an adaptive RS curve radius were set, and the node expansion method of Hybrid A* was altered. Based on the position of the node and the situation of surrounding obstacles, the dynamic expansion of the step size and the RS curve radius was achieved, enhancing the efficiency and flexibility of path search. A slope cost function was introduced, and the original cost function was improved, enabling the path to effectively avoid areas with significant terrain undulations, reducing the pitch and roll changes during the vehicle’s movement and minimizing the turning operations of the path. The path was smoothed through the gradient descent method, ensuring that the path did not collide with obstacles while reducing the change in curvature and improving the path quality. Simulation verification shows that the path planning algorithm with an adaptive step size Hybrid A*-adaptive RS curve radius can obtain shorter paths with smoother turns, demonstrating high efficiency and flexibility. The cost function taking slope and turning into consideration can effectively reduce the undulations of the path on unstructured roads, which is more conducive to vehicle tracking and control.