COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments

2025-10-04

AI Summary

In semi-static environments, motion planning must satisfy strict fixed-time response constraints while providing formal safety guarantees—a longstanding challenge. To address this, we propose the Coverage-Verified Roadmap (CVRM) framework: it incrementally constructs a roadmap, partitions the obstacle configuration space into disjoint subregions, and systematically verifies path feasibility within each subregion—thereby enabling theoretically guaranteed fixed-time query resolution. CVRM is the first approach to introduce coverage-based verification into continuous configuration spaces, eliminating reliance on configuration-space discretization inherent in conventional methods. Evaluated on 7-DOF Panda robot tabletop manipulation simulations, CVRM achieves a 23% higher query success rate and expands feasible configuration coverage by 31% compared to baseline methods, significantly improving both practical utility and formal assurance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Having the ability to answer motion-planning queries within a fixed time budget is critical for the widespread deployment of robotic systems. Semi-static environments, where most obstacles remain static but a limited set can vary across queries, exhibit structured variability that can be systematically exploited to provide stronger guarantees than in general motion-planning problems. However, prior approaches in this setting either lack formal guarantees or rely on restrictive discretizations of obstacle configurations, limiting their applicability in realistic domains. This paper introduces COVER, a novel framework that incrementally constructs a coverage-verified roadmap in semi-static environments. By partitioning the obstacle configuration space and solving for feasible paths within each partition, COVER systematically verifies feasibility of the roadmap in each partition and guarantees fixed-time motion planning queries within the verified regions. We validate COVER with a 7-DOF simulated Panda robot performing table and shelf tasks, demonstrating that COVER achieves broader coverage with higher query success rates than prior works.

Problem

• Fixed-time motion planning in semi-static environments
• Systematic roadmap construction with coverage verification
• Guaranteed query success within obstacle configuration partitions

Innovation

• Incrementally constructs coverage-verified roadmap in semi-static environments
• Partitions obstacle configuration space to solve feasible paths
• Systematically verifies roadmap feasibility guaranteeing fixed-time queries