Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators’ poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.

人机协作的最新进展使人类操作员和机器人可以在共享的制造环境中一起工作。但是，当前基于距离的无碰撞人机协作系统只能确保人员安全，而不能确保装配效率。在本文中，作者提出了一种基于上下文感知的无碰撞人机协作系统，该系统可以同时提供人身安全和组装效率。该系统可以规划机器人路径，避免与操作人员发生冲突，同时仍能及时到达目标位置。还可以以较低的计算成本来识别操作员的姿势，以进一步提高组装效率。为了支持上下文感知的无碰撞系统，提出并实现了带有传感器校准算法的完整碰撞感测模块。一种有效的基于转移学习的人体姿势识别算法也经过了调整和测试。设计了两个实验来测试所提出的人体姿势识别算法和整个系统的性能。结果表明整个系统的效率提高。