具有可穿越公差的行星探测车的改进路径规划和跟踪控制方法

Haojie Zhang, Feng Jiang, Qing Li
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引用次数: 0

摘要

为了保证行星探测车的安全、高效运行,行星探测车的路径规划与跟踪控制需要考虑复杂的三维地形特征、探测车自身的运动约束、可穿越性等因素。提出了一种改进的行星探测车在崎岖地形上的路径规划与跟踪控制方法。首先,建立了行星漫游车的运动学模型。生成了适应不同地形和探测车方向的三维运动原语库。利用运动原语和地形特征,改进了A*算法的状态展开过程和启发式函数。采用改进的A*算法生成全局路径,满足行星漫游车的运动学约束和三维地形约束。然后,根据行星漫游车的可穿越能力,设计了可选的圆弧路径集。每个弧线路径对应于一个特定的运动,决定了行星探测器的线速度和角速度。通过多目标评价函数选择最优路径。通过发送与实时避障最优路径相对应的最优指令,驱动行星漫游者精确跟踪全局路径。最后,通过两次仿真试验,在给定任务中有效验证了路径规划和跟踪控制方法。实验结果表明,改进的基于A*的算法比经典的A*算法减少了30.05%的规划时间,生成的路径更平滑。基于多目标弧线的方法提高了火星车的运动效率,确保火星车在全局路径上更安全、更快地完成任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved path planning and tracking control method for planetary exploration rovers with traversable tolerance
In order to ensure the safety and efficiency of planetary exploration rovers, path planning and tracking control of a planetary rover are expected to consider factors such as complex 3D terrain features, the motion constraints of the rover, traversability, etc. An improved path planning and tracking control method is proposed for planetary exploration rovers on rough terrain in this paper. Firstly, the kinematic model of the planetary rover is established. A 3D motion primitives library adapted to various terrains and the rover’s orientations is generated. The state expansion process and heuristic function of the A* algorithm are improved using the motion primitives and terrain features. Global path is generated by improved A*-based algorithm that satisfies the planetary rover’s kinematic constraints and the 3D terrain restrictions. Subsequently, an optional arc path set is designed based on the traversable capabilities of the planetary rover. Each arc path corresponds to a specific motion that determines the linear and angular velocities of the planetary rover. The optimal path is selected through the multi-objective evaluation function. The planetary rover is driven to accurately track the global path by sending optimal commands that corresponds to the optimal path for real-time obstacle avoidance. Finally, the path planning and tracking control method is effectively validated during a given mission through two simulation tests. The experiment results show that the improved A*-based algorithm reduces planning time by 30.05% and generates smoother paths than the classic A* algorithm. The multi-objective arc-based method improves the rover’s motion efficiency, ensuring safer and quicker mission completion along the global path.
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