A Minor-Testing Approach for Coordinated Motion Planning with Sliding Robots

We study a variant of the Coordinated Motion Planning problem on undirected graphs, referred to herein as the Coordinated Sliding-Motion Planning (CSMP) problem. In this variant, we are given an undirected graph G, k robots R₁,… ,R_k positioned on distinct vertices of G, p ≤ k distinct destination vertices for robots R₁,… ,R_p, and 𝓁 ∈ ℕ. The problem is to decide if there is a serial schedule of at most 𝓁 moves (i.e., of makespan 𝓁) such that at the end of the schedule each robot with a destination reaches it, where a robot’s move is a free path (unoccupied by any robots) from its current position to an unoccupied vertex. The problem is known to be NP-hard even on full grids. It has been studied in several contexts, including coin movement and reconfiguration problems, with respect to feasibility, complexity, and approximation. Geometric variants of the problem, in which congruent geometric-shape robots (e.g., unit disk/squares) slide or translate in the Euclidean plane, have also been studied extensively. We investigate the parameterized complexity of CSMP with respect to two parameters: the number k of robots and the makespan 𝓁. As our first result, we present a fixed-parameter algorithm for CSMP parameterized by k. For our second result, we present a fixed-parameter algorithm parameterized by 𝓁 for the special case of CSMP in which only a single robot has a destination and the graph is planar. A crucial new ingredient for both of our results is that the solution admits a succinct representation as a small labeled topological minor of the input graph.

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Publication Details

Journal
ArXiv.org
Published
2025-01-01
DOI
https://doi.org/10.4230/lipics.socg.2025.44
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00

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article

A Minor-Testing Approach for Coordinated Motion Planning with Sliding Robots

ArXiv.org
Modular Robots and Swarm Intelligence
article

A Minor-Testing Approach for Coordinated Motion Planning with Sliding Robots

article en

Abstract

We study a variant of the Coordinated Motion Planning problem on undirected graphs, referred to herein as the Coordinated Sliding-Motion Planning (CSMP) problem. In this variant, we are given an undirected graph G, k robots R₁,… ,R_k positioned on distinct vertices of G, p ≤ k distinct destination vertices for robots R₁,… ,R_p, and 𝓁 ∈ ℕ. The problem is to decide if there is a serial schedule of at most 𝓁 moves (i.e., of makespan 𝓁) such that at the end of the schedule each robot with a destination reaches it, where a robot’s move is a free path (unoccupied by any robots) from its current position to an unoccupied vertex. The problem is known to be NP-hard even on full grids. It has been studied in several contexts, including coin movement and reconfiguration problems, with respect to feasibility, complexity, and approximation. Geometric variants of the problem, in which congruent geometric-shape robots (e.g., unit disk/squares) slide or translate in the Euclidean plane, have also been studied extensively. We investigate the parameterized complexity of CSMP with respect to two parameters: the number k of robots and the makespan 𝓁. As our first result, we present a fixed-parameter algorithm for CSMP parameterized by k. For our second result, we present a fixed-parameter algorithm parameterized by 𝓁 for the special case of CSMP in which only a single robot has a destination and the graph is planar. A crucial new ingredient for both of our results is that the solution admits a succinct representation as a small labeled topological minor of the input graph.

ArXiv.org
DePaul University (US), TU Wien (AT), Royal Holloway University of London (GB), University of Warwick (GB)
Vienna Science and Technology Fund, Austrian Science Fund, Engineering and Physical Sciences Research Council
Sustainable cities and communities
Openalex Percentile: Top 54%
Modular Robots and Swarm Intelligence
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A Minor-Testing Approach for Coordinated Motion Planning with Sliding Robots · ArXiv.org (2025) | TGRS Research Map | TGRS