用物理仿真预判抓取后果，提升货架杂乱物品抓取成功率
Collapse and Collision Aware Grasping for Cluttered Shelf Picking
- 基于单张图像和深度图重建3D场景，结合物理仿真评估抓取策略
- 在真实堆叠场景中，成功率比传统方法提升超过20%
- 适合需要安全抓取的仓储机器人应用
仓库环境中高效安全地取出堆叠物品面临复杂空间依赖与结构关联的挑战。传统视觉方法虽能精确定位物体，却缺乏对提取操作可能引发碰撞或坍塌的物理推理能力。本文提出一种考虑坍塌与碰撞的抓取规划方法，通过动态物理仿真辅助机器人决策。利用单张图像和深度图重建场景的近似3D表示，并在仿真环境中评估不同提取策略。针对单盒取出与货架清空任务，分别提出基于启发式与物理仿真的两种方法。在结构化与非结构化箱堆的真实实验中，结合现有数据库的数据验证表明，该物理感知方法显著优于基线启发式方法，在效率与成功率上均有明显提升。
原文摘要 · Abstract (English)
Efficient and safe retrieval of stacked objects in warehouse environments is a significant challenge due to complex spatial dependencies and structural inter-dependencies. Traditional vision-based methods excel at object localization but often lack the physical reasoning required to predict the consequences of extraction, leading to unintended collisions and collapses. This paper proposes a collapse and collision aware grasp planner that integrates dynamic physics simulations for robotic decision-making. Using a single image and depth map, an approximate 3D representation of the scene is reconstructed in a simulation environment, enabling the robot to evaluate different retrieval strategies before execution. Two approaches 1) heuristic-based and 2) physics-based are proposed for both single-box extraction and shelf clearance tasks. Extensive real-world experiments on structured and unstructured box stacks, along with validation using datasets from existing databases, show that our physics-aware method significantly improves efficiency and success rates compared to baseline heuristics.
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