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This paper presents the first fully polynomial-time approximation schemes (FPTAS) for multi-robot motion planning in polygonal environments, resolving an open question regarding optimal pathfinding for a fixed number of robots. By generalizing previous results limited to two unit-square robots, the work offers substantially simpler algorithms that minimize total distance, maximum individual distance, or makespan across any fixed dimension. This advancement provides significant theoretical progress for computational geometry and robotics planning, establishing new efficiency bounds for collision-free motion in complex polygonal domains.
FPTAS for Polygonal Multi-Robot Motion Planning
arXiv:2610.20244v1 Announce Type: cross Abstract: We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain $\mathcal{W}$ with $n$ vertices and $k$ robots $R_1,\dots,R_k$ translating in $\mathcal{W}$ each of which is represented by a polygon of constant complexity. Given a source configuration $\mathbf{s}$ and a target configuration $\mathbf{t}$ of the robots in $\mathcal{W}$, the goal of the problem is to find a collision-free motion plan from $\mathbf{s}$ to $\mathbf{t}$ that is shortest with respect to some cost function. We present the first fully polynomial-time approximation schemes (FPTAS) for motion planning with any fixed number $k$ of robots, under the min-sum objective, the min-max objective, and the min-makespan objective. Here, the min-sum variant aims to minimize the total traveling distance of all robots, the min-max variant aims to minimize the maximum traveling distance of a single robot, and the min-makespan variant aims to minimize the total time cost under the assumption that all robots move with a maximum speed $1$. The algorithm directly generalizes to the problem in any fixed dimension $d$, with a polytope environment and polytope robots. Our result settles an open question of Agarwal, Halperin, Sharir, Steiger [SODA'24] and Agarwal, Holmgren, Steiger [SODA'26], who gave an FPTAS for $k=2$ unit-square robots and a bicriteria-approximation algorithm for a fixed number $k$ of unit-square or unit-disk robots, respectively. Compared to the previous results, our algorithm is substantially simpler.
Abstract:We study multi-robot motion planning in a polygonal environment with polygonal robots. The setting of the problem consists of a polygonal domain $\mathcal{W}$ with $n$ vertices and $k$ robots $R_1,\dots,R_k$ translating in $\mathcal{W}$ each of which is represented by a polygon of constant complexity. Given a source configuration $\mathbf{s}$ and a target configuration $\mathbf{t}$ of the robots in $\mathcal{W}$, the goal of the problem is to find a collision-free motion plan from $\mathbf{s}$ to $\mathbf{t}$ that is shortest with respect to some cost function. We present the first fully polynomial-time approximation schemes (FPTAS) for motion planning with any fixed number $k$ of robots, under the min-sum objective, the min-max objective, and the min-makespan objective. Here, the min-sum variant aims to minimize the total traveling distance of all robots, the min-max variant aims to minimize the maximum traveling distance of a single robot, and the min-makespan variant aims to minimize the total time cost under the assumption that all robots move with a maximum speed $1$. The algorithm directly generalizes to the problem in any fixed dimension $d$, with a polytope environment and polytope robots. Our result settles an open question of Agarwal, Halperin, Sharir, Steiger [SODA'24] and Agarwal, Holmgren, Steiger [SODA'26], who gave an FPTAS for $k=2$ unit-square robots and a bicriteria-approximation algorithm for a fixed number $k$ of unit-square or unit-disk robots, respectively. Compared to the previous results, our algorithm is substantially simpler.
Computational Geometry (cs.CG); Data Structures and Algorithms (cs.DS); Robotics (cs.RO)
arXiv:2610.20244 [cs.CG]
(or arXiv:2610.20244v1 [cs.CG] for this version)
https://doi.org/10.48550/arXiv.2610.20244 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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