论文标题
部分可观测时空混沌系统的无模型预测
Long-Horizon Motion Planning via Sampling and Segmented Trajectory Optimization
论文作者
论文摘要
本文提出了一个混合机器人运动计划者，该计划者在有障碍的环境中生成了长马运动计划，以实现机器人导航。我们提出了一个混合计划者RRT*，具有分段轨迹优化（RRT* -SOPT），该曲线优化（RRT* -SOPT）结合了基于抽样计划，基于优化的计划和轨迹拆分的优点，以快速计划无碰撞且动态的可行的运动计划。当生成计划时，RRT*层快速采样半最佳路径并将其设置为初始参考路径。然后，SOPT层拆分参考路径并在每个段上执行优化。然后，它再次将新轨迹分开，然后重复该过程，直到整个轨迹收敛为止。我们还建议在收敛之前减少片段的数量，以进一步减少计算时间。仿真结果表明，RRT*-SOPT从轨迹分裂的混合结构中受益，并在各种机器人平台和场景中表现出色。
This paper presents a hybrid robot motion planner that generates long-horizon motion plans for robot navigation in environments with obstacles. We propose a hybrid planner, RRT* with segmented trajectory optimization (RRT*-sOpt), which combines the merits of sampling-based planning, optimization-based planning, and trajectory splitting to quickly plan for a collision-free and dynamically-feasible motion plan. When generating a plan, the RRT* layer quickly samples a semi-optimal path and sets it as an initial reference path. Then, the sOpt layer splits the reference path and performs optimization on each segment. It then splits the new trajectory again and repeats the process until the whole trajectory converges. We also propose to reduce the number of segments before convergence with the aim of further reducing computation time. Simulation results show that RRT*-sOpt benefits from the hybrid structure with trajectory splitting and performs robustly in various robot platforms and scenarios.
