Mutual-cognition for proactive human–robot collaboration: A mixed reality-enabled visual reasoning-based method
提出一种混合现实与视觉推理结合的互认知方法，通过数字孪生监控和视觉场景理解，实现人机任务分配与主动协作，提升灵活自动化水平。
Human-Robot Collaboration (HRC) is key to flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and less ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins (DTs) states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human-robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries (EVBs), the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation.