Safe and efficient collaboration among multiple robots in unstructured environments is increasingly critical in the era of Industry 4.0. However, achieving robust and autonomous collaboration among humans and other robots requires modern robotic systems to have effective proximity perception and reactive obstacle avoidance. In this paper, we propose a novel methodology for reactive whole-body obstacle avoidance that ensures conflict-free robot-robot interactions even in dynamic environment. Unlike existing approaches based on Jacobian-type, sampling based or geometric techniques, our methodology leverages the latest deep learning advances and topological manifold learning, enabling it to be readily generalized to other problem settings with high computing efficiency and fast graph traversal techniques. Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact, a significant improvement over traditional industrial cobot settings. To validate our approach, we implement it on a robotic platform consisting of dual 6-DoF robotic arms with optimized proximity sensor placement, capable of working collaboratively with varying levels of interference. Specifically, one arm performs reactive whole-body obstacle avoidance while achieving its pre-determined objective, while the other arm emulates the presence of a human collaborator with independent and potentially adversarial movements. Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.

翻译：在工业4.0时代，多机器人在非结构化环境中的安全高效协作日益关键。然而，要实现机器人与人及其他机器人间的稳健自主协作，现代机器人系统需具备有效的近距感知与反应式避障能力。本文提出一种用于反应式全身避障的新方法，即使动态环境中也能确保无冲突的机器人间交互。与基于雅可比矩阵、采样或几何技术的现有方法不同，本方法利用深度学习最新进展与拓扑流形学习，能以高计算效率与快速图遍历技术便捷地推广至其他问题场景。该方法使机械臂可在无直接接触的情况下主动规避任意三维形状的障碍物，这是对传统工业协作机器人设置的重要改进。为验证本方法，我们在配备优化近距传感器布局的双六自由度机械臂机器人平台上进行实现，该平台能支持不同干扰级别的协同作业。具体而言，一只机械臂在执行预设目标的同时实施反应式全身避障，另一只机械臂则通过独立且可能具有对抗性的运动模拟人类协作者。本方法为非平稳环境下的人机安全协作提供了稳健有效的解决方案。