Robust multi-layered sampling-based path planning for temporal logic-based missions
Robust multi-layered sampling-based path planning for temporal logic-based missions
复制标题
针对基于时间逻辑的任务的稳健的多层基于采样的路径规划
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Songhwai Oh
中科院分区：
文献类型：
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作者：
Yoonseon Oh;Kyunghoon Cho;Yunho Choi;Songhwai Oh
我们研究了一种用于生成健壮和安全路径的路径计划算法，该算法满足了线性时间逻辑（LTL）中指定的任务要求。当部署机器人执行任务时，可能会有可能导致任务失败或遇到障碍发生碰撞的干扰。因此，计划算法需要考虑安全性和鲁棒性，以防止可能的干扰。我们提出了一种强大的路径计划算法，该算法通过考虑机器人动力学的干扰，同时最大程度地降低机器人的移动距离，从而最大程度地提高了成功完成任务的可能性。所提出的方法可以通过层次结合使用LTL公式和机会限制来确保计划轨迹的安全性。高级策划者生成了满足LTL指定任务要求的离散计划。低级计划者建立了基于抽样的RRT搜索树，以最大程度地减少任务故障概率和移动距离，同时保证与障碍物碰撞的概率低于指定的阈值。我们验证了使用四极管在模拟和实验中算法产生的路径的鲁棒性和安全性。
We investigate a path planning algorithm for generating robust and safe paths, which satisfy mission requirements specified in linear temporal logic (LTL). When robots are deployed to perform a mission, there can be disturbances which can cause mission failures or collisions with obstacles. Hence, a path planning algorithm needs to consider safety and robustness against possible disturbances. We present a robust path planning algorithm, which maximizes the probability of success in accomplishing a given mission by considering disturbances in robot dynamics while minimizing the moving distance of a robot. The proposed method can guarantee the safety of the planned trajectory by incorporating an LTL formula and chance constraints in a hierarchical manner. A high-level planner generates a discrete plan satisfying the mission requirements specified in LTL. A low-level planner builds a sampling-based RRT search tree to minimize both the mission failure probability and the moving distance while guaranteeing the probability of collision with obstacles to be below a specified threshold. We validate the robustness and safety of paths generated by the algorithm in simulation and experiments using a quadrotor.