让机器人在动态环境中安全抓取，兼顾视野与移动的平衡。
Visibility-Aware Mobile Grasping in Dynamic Environments
- 用主动感知与迭代规划，边看边动避免碰撞。
- 仿真和真实场景中成功率分别达68.8%和58.0%。
- 适合复杂动态环境下的移动抓取任务研究者。
本文解决在未知动态环境中，受限视场下移动抓取的问题。核心挑战在于：扩大视野以降低环境不确定性，与移动身体推进任务之间存在权衡，且受可视性约束。以往方法常假设环境已知或静态，分离视觉与运动目标，在动态障碍物遮挡路径时无法保证安全。本文提出统一移动抓取系统，包含两个核心模块：(1) 基于速度感知的主动感知迭代低层全肢体规划器，实现动态环境安全导航；(2) 基于行为树的分层高层规划器，自适应生成子目标，引导机器人完成探索与运行时故障处理。在400组随机仿真场景及真实世界Fetch移动机械臂部署中验证，系统在未知静态环境成功率68.8%，动态环境58.0%，较基线方法分别提升22.8%和18.0%，并显著改善碰撞安全性。
原文摘要 · Abstract (English)
This paper addresses the problem of mobile grasping in dynamic, unknown environments where a robot must operate under a limited field-of-view. The fundamental challenge is the inherent trade-off between ``seeing'' around to reduce environmental uncertainty and ``moving'' the body to achieve task progress in a high-dimensional configuration space, subject to visibility constraints. Previous approaches often assume known or static environments and decouple these objectives, failing to guarantee safety when unobserved dynamic obstacles intersect the robot's path during manipulation. In this paper, we propose a unified mobile grasping system comprising two core components: (1) an iterative low-level whole-body planner coupled with velocity-aware active perception to navigate dynamic environments safely; and (2) a hierarchical high-level planner based on behavior trees that adaptively generates subgoals to guide the robot through exploration and runtime failures. We provide experimental results across 400 randomized simulation scenarios and real-world deployment on a Fetch mobile manipulator. Results show that our system achieves a success rate of 68.8\% and 58.0\% in unknown static and dynamic environments, respectively, significantly boosting success rates by 22.8\% and 18.0\% over the \nam approach in both unknown static and dynamic environments, with improved collision safety.
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