论文标题
基于搜索的在线轨迹计划在高度动态的环境中类似汽车的机器人
Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments
论文作者
论文摘要
本文提出了一个基于搜索的部分运动计划者，以在高度动态的环境中为类似汽车的机器人生成动态可行的轨迹。策划者通过探索建立在运动原语的状态图中来搜索平滑，安全和近距离最佳的轨迹，该状态图是通过离散时间维度和控制空间来生成的。为了启用快速的在线计划，我们首先根据运动原语的聚合和修剪来提出有效的路径搜索算法。然后，我们提出了一种快速碰撞检查算法，该算法考虑了移动障碍的动作。该算法将机器人与障碍物之间的相对运动线性化，然后通过比较点线距离来检查碰撞。从快速的搜索和碰撞检查算法中受益，规划师可以有效，安全地探索国家时间空间，以生成近时间最佳的解决方案。通过广泛的实验的结果表明，所提出的方法可以在毫秒内产生可行的轨迹，同时保持比最新方法更高的成功率，这显着证明了其优势。
This paper presents a search-based partial motion planner to generate dynamically feasible trajectories for car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives, which are generated by discretizing the time dimension and the control space. To enable fast online planning, we first propose an efficient path searching algorithm based on the aggregation and pruning of motion primitives. We then propose a fast collision checking algorithm that takes into account the motions of moving obstacles. The algorithm linearizes relative motions between the robot and obstacles and then checks collisions by comparing a point-line distance. Benefiting from the fast searching and collision checking algorithms, the planner can effectively and safely explore the state-time space to generate near-time-optimal solutions. The results through extensive experiments show that the proposed method can generate feasible trajectories within milliseconds while maintaining a higher success rate than up-to-date methods, which significantly demonstrates its advantages.
