This article introduces a multimodal motion planning (MMP) algorithm that combines three-dimensional (3-D) path planning and a DWA obstacle avoidance algorithm. The algorithms aim to plan the path and motion of obstacle-overcoming robots in complex unstructured scenes. A novel A-star algorithm is proposed to combine the characteristics of unstructured scenes and a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile, the algorithm of path planning is integrated with the DWA algorithm so that the robot can perform local dynamic obstacle avoidance during the movement along the global planned path. Furthermore, when the proposed global path planning algorithm combines with the local obstacle avoidance algorithm, the robot can correct the path after obstacle avoidance and obstacle overcoming. The simulation experiments in a factory with several complex environments verified the feasibility and robustness of the algorithms. The algorithms can quickly generate a reasonable 3-D path for obstacle-overcoming robots and perform reliable local obstacle avoidance under the premise of considering the characteristics of the scene and motion obstacles.

翻译：本条引入了将三维(3-D)路径规划和DWA障碍避免算法相结合的多式联运运动规划算法(MMP),该算法的目的是在复杂的非结构化场景中规划障碍克服机器人的路径和运动。提出了一个新的A星算法,将非结构化场景的特征和将其转换为贪婪的最佳第一战略算法的战略结合起来。与此同时,路径规划算法与DWA算法相结合,使机器人在沿全球计划路径移动的过程中能够避免当地动态障碍。此外,当拟议的全球路径规划算法与当地障碍避免算法相结合时,机器人可以在避免障碍和障碍克服障碍之后纠正路径。在几个复杂环境下的工厂进行的模拟实验验证了算法的可行性和稳健性。该算法可以迅速为克服障碍的机器人创造合理的三维路径,并在考虑现场和运动障碍特点的前提下进行可靠的地方障碍避免。