Automated Generation of Continuous-Space Roadmaps for Routing Mobile Robot Fleets

TL;DR

This paper introduces an automated continuous-space roadmap generation method for mobile robot fleet routing, improving geometric accuracy and practical constraint adherence to enhance efficiency and robustness in intralogistics.

Contribution

It presents a novel approach combining free space discretization, demand-driven path optimization, and smoothing to generate tailored roadmaps for intralogistics applications.

Findings

Outperforms grid-based baselines in path length and complexity

Achieves higher redundancy and robustness in routing

Produces near-optimal paths with lower structural complexity

Abstract

Efficient routing of mobile robot fleets is crucial in intralogistics, where delays and deadlocks can substantially reduce system throughput. Roadmap design, specifying feasible transport routes, directly affects fleet coordination and computational performance. Existing approaches are either grid-based, compromising geometric precision, or continuous-space approaches that disregard practical constraints. This paper presents an automated roadmap generation approach that bridges this gap by operating in continuous-space, integrating station-to-station transport demand and enforcing minimum distance constraints for nodes and edges. By combining free space discretization, transport demand-driven $K$-shortest-path optimization, and path smoothing, the approach produces roadmaps tailored to intralogistics applications. Evaluation across multiple intralogistics use cases demonstrates that the proposed approach consistently outperforms established baselines (4-connected grid, 8-connected grid, and random sampling), achieving lower structural complexity, higher redundancy, and near-optimal path lengths, enabling efficient and robust routing of mobile robot fleets.