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

2025-11-10

AI Summary

Existing path planning methods for mobile robot fleets in warehouse logistics suffer from low efficiency, deadlock susceptibility, and difficulty balancing geometric accuracy with operational constraints. Method: This paper proposes a demand-driven automatic roadmap generation method in continuous space. It jointly models inter-station transportation demand and minimum safety distance constraints within the continuous domain, followed by free-space discretization, K-shortest-path optimization, and trajectory smoothing to construct a compact, low-redundancy roadmap with high geometric fidelity and compliance with real-world kinematic and safety constraints. Contribution/Results: Experiments across diverse warehouse scenarios demonstrate that the generated roadmap yields near-optimal paths and significantly improves scheduling efficiency compared to 4-/8-connected grid and random sampling approaches. System throughput increases markedly, and robustness—particularly against deadlocks and congestion—is substantially enhanced.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Automated continuous-space roadmap generation for mobile robot fleets
• Bridging grid-based and continuous-space approaches with practical constraints
• Optimizing routing efficiency while avoiding delays and deadlocks

Innovation

• Continuous-space roadmap generation for mobile robots
• Integrates transport demand and minimum distance constraints
• Combines discretization, K-shortest-path optimization and smoothing