论文标题
使用多级摩尔斯理论在多机器人运动计划中可视化本地最小值
Visualizing Local Minima in Multi-Robot Motion Planning using Multilevel Morse Theory
论文作者
论文摘要
多机器人运动计划问题通常具有许多本地最小值。将这些本地最小值可视化至关重要，以便我们可以更好地理解，调试并与多机器人系统进行交互。为了实现这一目标，我们介绍了多机器人运动资源管理器，这是一种算法，通过引入基于组件的框架来扩展多级摩尔斯理论的先前结果，我们通过使用光纤包装来减少每个机器人组件空间来减少多机器人配置空间。我们的算法利用了此组件结构来搜索和可视化局部最小值。该算法的用户可以指定多级抽象和优化算法。我们使用此信息来逐步构建针对给定问题的本地最小树。我们在多个具有20个自由度的多机器人系统上证明了这种算法。
Multi-robot motion planning problems often have many local minima. It is essential to visualize those local minima such that we can better understand, debug and interact with multi-robot systems. Towards this goal, we present the multi-robot motion explorer, an algorithm which extends previous results on multilevel Morse theory by introducing a component-based framework, where we reduce multi-robot configuration spaces by reducing each robots component space using fiber bundles. Our algorithm exploits this component structure to search for and visualize local minima. A user of the algorithm can specify a multilevel abstraction and an optimization algorithm. We use this information to incrementally build a local minima tree for a given problem. We demonstrate this algorithm on several multi-robot systems of up to 20 degrees of freedom.
