arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2605.02487cs.RO

动态环境中具有可视性的移动抓取

Visibility-Aware Mobile Grasping in Dynamic Environments

School of Computing, National University of Singapore(新加坡国立大学计算机学院);Smart Systems Institute, National University of Singapore(新加坡国立大学智能系统研究所)

Tianrun Hu, Anxing Xiao, David Hsu, Hanbo Zhang

AI总结:

本文提出一种统一的移动抓取系统,通过迭代低层全身规划器和基于行为树的高层规划器,提升动态环境中的抓取成功率和安全性。

摘要:

本文针对动态、未知环境中机器人受限视野下的移动抓取问题,提出一个统一的移动抓取系统,包含迭代低层全身规划器与速度感知主动感知相结合的导航模块,以及基于行为树的分层高层规划器,通过自适应生成子目标引导机器人进行探索和运行时故障处理。实验结果显示,在未知静态和动态环境中,系统分别达到68.8%和58.0%的成功率,显著提升成功率并改善碰撞安全性。

英文摘要:

We consider mobile grasping in unknown and dynamic environments, where a robot must approach and grasp a target object using a camera with a limited field of view. This setting introduces two visibility constraints. Incomplete environmental observations can create collision risks along the whole-body trajectory, while occlusions can hinder object pose refinement during grasping. Previous systems often assume known or static environments and decouple perception from motion, leaving safety unresolved around unobserved obstacles. We present a unified system that enforces both visibility constraints at runtime through three core components. A velocity-aware gaze policy directs perception toward fast-moving regions of the planned swept volume, a hierarchical subgoal policy samples grasp and intermediate configurations that maintain target visibility, and a real-time whole-body planner replans over a receding horizon as the map updates. Across 400 randomized simulation scenarios and real-world trials on a Fetch mobile manipulator, our system achieves success rates of 70.4% in unknown static environments and 57.9% in dynamic environments, outperforming the decoupled navigation- and-manipulation strategy by 16.3 and 9.3 percentage points, respectively, while significantly reducing collisions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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