Title: K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning Show Chinese title

Title: K-ARC：多机器人动力学规划的自适应机器人协作

Abstract: This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning. Our experimental results show the capability of K-ARC to plan for up to 32 planar mobile robots, while achieving up to an order of magnitude of speed-up compared to previous methods in various scenarios. K-ARC is able to achieve this due to its two main properties. First, K-ARC constructs its solution iteratively by planning in segments, where initial kinodynamic paths are found through optimization-based approaches and the inter-robot conflicts are resolved through sampling-based approaches. The interleaving use of sampling-based and optimization-based approaches allows K-ARC to leverage the strengths of both approaches in different sections of the planning process where one is more suited than the other, while previous methods tend to emphasize on one over the other. Second, K-ARC builds on a previously proposed multi-robot motion planning framework, Adaptive Robot Coordination (ARC), and inherits its strength of focusing on coordination between robots only when needed, saving computation efforts. We show how the combination of these two properties allows K-ARC to achieve overall better performance in our simulated experiments with increasing numbers of robots, increasing degrees of problem difficulties, and increasing complexities of robot dynamics. Show Chinese abstract

Abstract: 这项工作提出了动力学自适应机器人协调（K-ARC），一种用于多机器人动力学规划的新算法。 我们的实验结果表明，K-ARC能够为多达32个平面移动机器人进行规划，在各种场景中相比之前的方法速度提高了数量级。 K-ARC能够实现这一点是由于其两个主要特性。 首先，K-ARC通过分段规划来迭代地构建其解决方案，其中初始的动力学路径通过基于优化的方法找到，而机器人之间的冲突则通过基于采样的方法解决。 采样方法和优化方法的交替使用使K-ARC能够在规划过程的不同部分利用两种方法的优势，其中一种方法比另一种更合适，而之前的方法往往倾向于强调其中一种方法。 其次， K-ARC建立在之前提出的多机器人运动规划框架自适应机器人协调（ARC）之上，并继承了其仅在需要时关注机器人之间协调的优势，从而节省计算资源。 我们展示了这两种特性的结合如何使K-ARC在我们的模拟实验中，随着机器人数量的增加、问题难度的增加以及机器人动力学复杂性的增加，整体性能得到提升。