两机器人协作时，如何规划辅助机器人的移动时机以最大化任务支持范围。
Offline Task Assistance Planning on a Graph:Theoretic and Algorithmic Foundations
- 将辅助机器人路径约束为固定路线，仅优化移动时间节点
- 在固定路径下可多项式时间求得最优移动时机
- 适用于真实机器人实验与复杂仿真场景
本文提出任务协助规划问题，给定执行任务的机器人Rtask和提供辅助的Rassist。Rassist通过车载传感器协助Rtask，其协助能力取决于两机器人的相对位置。由于Rtask沿预设路径移动，Rassist需调整位置以最大化协助覆盖范围。研究限定Rassist在配置空间中嵌入的路网（图）上移动，证明该问题为NP难。但若Rassist路径已知，仅需计算在各节点间的移动时机，则可在多项式时间内求解最优。结合精心设计的上界，该方法被整合进分支定界算法，显著优于基线方法。在包含平面机械臂和UR机器人的仿真以及真实机器人实验中均验证了有效性。
原文摘要 · Abstract (English)
In this work we introduce the problem of task assistance planning where we are given two robots Rtask and Rassist. The first robot, Rtask, is in charge of performing a given task by executing a precomputed path. The second robot, Rassist, is in charge of assisting the task performed by Rtask using on-board sensors. The ability of Rassist to provide assistance to Rtask depends on the locations of both robots. Since Rtask is moving along its path, Rassist may also need to move to provide as much assistance as possible. The problem we study is how to compute a path for Rassist so as to maximize the portion of Rtask's path for which assistance is provided. We limit the problem to the setting where Rassist moves on a roadmap which is a graph embedded in its configuration space and show that this problem is NP-hard. Fortunately, we show that when Rassist moves on a given path, and all we have to do is compute the times at which Rassist should move from one configuration to the following one, we can solve the problem optimally in polynomial time. Together with carefully-crafted upper bounds, this polynomial-time algorithm is integrated into a Branch and Bound-based algorithm that can compute optimal solutions to the problem outperforming baselines by several orders of magnitude. We demonstrate our work empirically in simulated scenarios containing both planar manipulators and UR robots as well as in the lab on real robots.
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