Universal Solvability for Robot Motion Planning on Graphs
Anubhav Dhar, Pranav Nyati, Tanishq Prasad, Ashlesha Hota, Sudeshna Kolay

TL;DR
This paper investigates the conditions under which robot motion planning on graphs is universally solvable, introduces algorithms for configuration reachability, and explores graph augmentation strategies to achieve universal solvability.
Contribution
It presents a canonical procedure for analyzing configuration reachability, efficient algorithms for determining solvability, and bounds on graph augmentation needed for universal solvability.
Findings
At least half of configurations are unreachable in non-universally solvable instances.
An efficient randomized algorithm with one-sided error is developed for USolR.
Upper bounds on edges needed for graph augmentation to achieve universal solvability are provided.
Abstract
We study the Universal Solvability of Robot Motion Planning on Graphs (USolR) problem: given an undirected graph and robots, determine whether any arbitrary configuration of the robots can be transformed into any other arbitrary configuration via a sequence of valid, collision-free moves. We design a canonical accumulation procedure that maps arbitrary configurations to configurations that occupy a fixed subset of vertices, enabling us to analyze configuration reachability in terms of equivalence classes. We prove that in instances that are not universally solvable, at least half of all configurations are unreachable from a given one, and leverage this to design an efficient randomized algorithm with one-sided error, which can be derandomized with a blow-up in the running time by a factor of . Further, we optimize our deterministic algorithm by using the structure of…
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