让协作机器人实时感知工具并快速避障，提升动态环境安全性。
Learning Fast, Tool aware Collision Avoidance for Collaborative Robots
- 基于学习的感知模型实时识别工具与障碍物，处理遮挡与部分观测。
- 避障控制在10毫秒内完成，实测精度达亚毫米级，优于传统方法。
- 计算成本降低60%，适合实际部署于工业协作场景。
在人机共存的动态环境中，确保协作机器人安全高效运行极具挑战，尤其当障碍物运动和任务频繁变化时。现有控制器通常假设完全可见且工具固定，易导致碰撞或过度保守行为。本文提出一种工具感知的实时避障系统，可自适应不同工具尺寸及工具-环境交互模式。通过学习型感知模型，系统从点云中分离出机器人与工具成分，推理被遮挡区域，并在部分可观测条件下预测碰撞。随后采用约束强化学习训练的控制策略，在10毫秒内生成平滑避障动作。在仿真与真实测试中，本方法在动态环境中优于传统算法（APF、MPPI），同时保持亚毫米级精度。此外，相比最先进GPU规划器，本系统计算开销降低约60%。该方法模块化、高效且有效，已集成至协作机器人应用中，验证了其在安全、实时操作中的实用性。
原文摘要 · Abstract (English)
Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly conservative behavior. In our work, we introduce a tool-aware collision avoidance system that adjusts in real time to different tool sizes and modes of tool-environment interaction. Using a learned perception model, our system filters out robot and tool components from the point cloud, reasons about occluded area, and predicts collision under partial observability. We then use a control policy trained via constrained reinforcement learning to produce smooth avoidance maneuvers in under 10 milliseconds. In simulated and real-world tests, our approach outperforms traditional approaches (APF, MPPI) in dynamic environments, while maintaining sub-millimeter accuracy. Moreover, our system operates with approximately 60% lower computational cost compared to a state-of-the-art GPU-based planner. Our approach provides modular, efficient, and effective collision avoidance for robots operating in dynamic environments. We integrate our method into a collaborative robot application and demonstrate its practical use for safe and responsive operation.
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