← 研踪 PaperTrailarXiv:2603.16510 · v2
Simplified Algorithms and Improved Bounds for Motion Planning
首次提交 2026-03-17 21:38
版本更新 2026-10-28 17:35
雷达观测 2026-10-30 10:22
摘要
We investigate coordinated motion planning (CMP) for unit square robots in the plane under the $L_1$ metric. Given two arrangements of $k$ robots, we seek a movement schedule minimizing one of three objectives: the Min-Sum objective (sum of distances), the Min-Makespan objective (latest time of arrival) and the Min-Exposure objective (a new objective). For Min-Sum and Min-Makespan, we provide a simplified approach and improved bounds, compared to Eiben, Ganian and Kanj (SoCG 2023). To this end, we partition the configuration space based on the relative ordering of the robots. This leads to a simple and efficient framework to discretize the search space. Our results extend to several settings in the plane, including convex robots in convex domains and vertex robots in rectangular grids. Finally, we apply our results to the new Min-Exposure objective. Given a set of polygonal regions that provide cover, the task is to find a schedule with minimal elapsed time during which at least one robot is not fully covered. We give an $\mathcal{O}(n^4\log n)$ time algorithm that computes exposure-minimal schedules for $k=2$ robots, and an XP algorithm for arbitrary $k$.
作者备注:10 pages, 5 figures
观测到的版本
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v2 · 2026-10-28 17:35 · 最新观测版本
Simplified Algorithms and Improved Bounds for Motion Planning
We investigate coordinated motion planning (CMP) for unit square robots in the plane under the $L_1$ metric. Given two arrangements of $k$ robots, we seek a movement schedule minimizing one of three objectives: the Min-Sum objective (sum of distances), the Min-Makespan objective (latest time of arrival) and the Min-Exposure objective (a new objective). For Min-Sum and Min-Makespan, we provide a simplified approach and improved bounds, compared to Eiben, Ganian and Kanj (SoCG 2023). To this end, we partition the configuration space based on the relative ordering of the robots. This leads to a simple and efficient framework to discretize the search space. Our results extend to several settings in the plane, including convex robots in convex domains and vertex robots in rectangular grids. Finally, we apply our results to the new Min-Exposure objective. Given a set of polygonal regions that provide cover, the task is to find a schedule with minimal elapsed time during which at least one robot is not fully covered. We give an $\mathcal{O}(n^4\log n)$ time algorithm that computes exposure-minimal schedules for $k=2$ robots, and an XP algorithm for arbitrary $k$.
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