两机器人协同规划，最大化辅助时间
Joint Task Assistance Planning via Nested Branch and Bound (Extended Version)
- 用嵌套分支定界法分层搜索路径组合
- 相比基线方法提速最高达100倍
- 适合需要高效协同的机器人任务规划
我们提出并研究联合任务协助规划问题，该问题推广了以往关于机器人协作中辅助优化的工作。在此设定下，两个机器人在预定义的路径图上运行，每个路径图对应其配置空间的图结构。一个任务机器人需执行定时任务，另一个协助机器人则根据两者空间关系提供基于传感器的支持。目标是计算出两条机器人的路径，以最大化总辅助时长。由于路径组合存在组合爆炸，且问题具有时间依赖性（需考虑时间因素），求解极具挑战。为此，我们提出一种嵌套分支定界框架，以分层方式高效探索机器人路径空间。我们对算法进行实证评估，结果表明相比基线方法，速度提升高达两个数量级。
原文摘要 · Abstract (English)
We introduce and study the Joint Task Assistance Planning problem which generalizes prior work on optimizing assistance in robotic collaboration. In this setting, two robots operate over predefined roadmaps, each represented as a graph corresponding to its configuration space. One robot, the task robot, must execute a timed mission, while the other, the assistance robot, provides sensor-based support that depends on their spatial relationship. The objective is to compute a path for both robots that maximizes the total duration of assistance given. Solving this problem is challenging due to the combinatorial explosion of possible path combinations together with the temporal nature of the problem (time needs to be accounted for as well). To address this, we propose a nested branch-and-bound framework that efficiently explores the space of robot paths in a hierarchical manner. We empirically evaluate our algorithm and demonstrate a speedup of up to two orders of magnitude when compared to a baseline approach.
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