为可变障碍物环境设计固定时间规划的可靠路径图框架
COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments
- 分块处理可动障碍物配置空间，逐区域验证路径可行性
- 在7自由度机械臂上实现更高查询成功率和更广覆盖范围
- 适合对实时性与路径可靠性要求高的工业场景
在时间敏感的应用中，以固定时间预算解决运动规划问题至关重要。半静态环境（大部分工作空间固定，仅部分障碍物随任务变化）具有结构化变异特性，可被利用以提供强于通用规划器的保障。然而，现有方法要么缺乏形式化覆盖保证，要么依赖障碍物配置的离散化，限制了在真实场景中的适用性。本文提出COVER框架，通过独立划分每个可动障碍物的配置空间，增量构建具备覆盖验证的路径图，并在各分区中验证路径可行性，实现对已验证区域的固定时间查询。我们在7自由度机械臂执行桌面与货架物体抓取任务中评估COVER，结果表明其在不同尺寸障碍物下相比已有方法展现出更广的问题空间覆盖和更高的查询成功率。
原文摘要 · Abstract (English)
The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Semi-static environments, where most of the workspace remains fixed while a subset of obstacles varies between tasks, exhibit structured variability that can be exploited to provide stronger guarantees than general-purpose planners. However, existing approaches either lack formal coverage guarantees or rely on discretizations of obstacle configurations that restrict applicability to realistic domains. This paper introduces COVER, a framework that incrementally constructs coverage-verified roadmaps for semi-static environments. COVER decomposes the arrangement space by independently partitioning the configuration space of each movable obstacle and verifies roadmap feasibility within each partition, enabling fixed-time query resolution for verified regions.We evaluate COVER on a 7-DoF manipulator performing object-picking in tabletop and shelf environments, demonstrating broader problem-space coverage and higher query success rates than prior work, particularly with obstacles of different sizes.
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