论文标题
多树指导有效的机器人运动计划
Multi-Tree Guided Efficient Robot Motion Planning
论文作者
论文摘要
机器人完成不同任务是必要的。由于它们在状态空间中的快速搜索，快速探索的随机树（RRT）及其变体已被广泛用于机器人运动计划。但是，它们在许多复杂的环境中的表现不佳，因为运动计划需要同时考虑几何约束和差异约束。在本文中，我们提出了一种新型的机器人运动计划算法，该算法利用多树来指导探索和剥削。提出的算法一开始维护两棵树以搜索状态空间。每棵树将探索本地环境。树从根开始，将逐渐从其他树木中收集信息，并朝目标状态生长。这种同时探索和剥削方法可以迅速找到可行的轨迹。我们将所提出的算法与其他流行的运动计划算法进行比较。实验结果表明，我们的算法在不同的评估指标上实现了最佳性能。
Motion Planning is necessary for robots to complete different tasks. Rapidly-exploring Random Tree (RRT) and its variants have been widely used in robot motion planning due to their fast search in state space. However, they perform not well in many complex environments since the motion planning needs to simultaneously consider the geometry constraints and differential constraints. In this article, we propose a novel robot motion planning algorithm that utilizes multi-tree to guide the exploration and exploitation. The proposed algorithm maintains more than two trees to search the state space at first. Each tree will explore the local environment. The tree starts from the root will gradually collect information from other trees and grow towards the goal state. This simultaneous exploration and exploitation method can quickly find a feasible trajectory. We compare the proposed algorithm with other popular motion planning algorithms. The experiment results demonstrate that our algorithm achieves the best performance on different evaluation metrics.
