Lazy Reconfiguration Forest (LRF) - An Approach for Motion Planning with Multiple Tasks in Dynamic Environments
Lazy Reconfiguration Forest (LRF) - An Approach for Motion Planning with Multiple Tasks in Dynamic Environments
复制标题
惰性重配置森林 (LRF) - 动态环境中多任务运动规划的方法
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
P. Xavier
中科院分区：
文献类型：
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作者：
Russell Gayle;Kristopher R. Klingler;P. Xavier
提出了一种动态环境下机器人运动规划的新算法。我们的方法在几个方面扩展了快速探索随机树(RRT)。我们假设需要针对动态环境中移动机器人的当前状态同时规划和维护多个任务的路径。我们的算法通过动态地分割、生长和合并树木来动态地维护树木森林，以适应移动的障碍物和机器人的运动。为了最大限度地减少树的维护，我们只验证任务路径，而不是整个森林。有人居住的树的根部随着机器人移动。动态重新规划与树木和森林维护相结合。将机器人运动与规划器相结合，使我们能够支持多项任务，例如，在向目标移动时提供一条“逃生”路径。机器人可以自由地沿着它选择的任何任务路径移动。我们通过在具有移动障碍物的模拟环境中显示快速结果来突出这项工作。
We present a novel algorithm for robot motion planning in dynamic environments. Our approach extends rapidly-exploring random trees (RRTs) in several ways. We assume the need to simultaneously plan and maintain paths for multiple tasks with respect to the current state of a moving robot in a dynamic environment. Our algorithm dynamically maintains a forest of trees by splitting, growing and merging them on the fly to adapt to moving obstacles and robot motion. In order to minimize tree maintenance, we only validate the task paths, rather than the entire forest. The root of the inhabited tree moves with the robot. Dynamic re-planning is integrated with tree and forest maintenance. Coupling the robot motion with the planner enables us to support multiple tasks, for example providing an "escape" path while moving to a goal. The robot is free to move along whichever task path it chooses. We highlight the work by showing fast results in simulated environments with moving obstacles.