In this study, we present an optimization framework for efficient motion priority design between automated and teleoperated robots in an industrial recovery scenario. Although robots have recently become increasingly common in industrial sites, there are still challenges in achieving human-robot collaboration/cooperation (HRC), where human workers and robots are engaged in collaborative and cooperative tasks in a shared workspace. For example, the corresponding factory cell must be suspended for safety when an industrial robot drops an assembling part in the workspace. After that, a human worker is allowed to enter the robot workspace to address the robot recovery. This process causes non-continuous manufacturing, which leads to a productivity reduction. Recently, robotic teleoperation technology has emerged as a promising solution to enable people to perform tasks remotely and safely. This technology can be used in the recovery process in manufacturing failure scenarios. Our proposition involves the design of an appropriate priority function that aids in collision avoidance between the manufacturing and recovery robots and facilitates continuous processes with minimal production loss within an acceptable risk level. This paper presents a framework, including an HRC simulator and an optimization formulation, for finding optimal parameters of the priority function. Through quantitative and qualitative experiments, we address the proof of our novel concept and demonstrate its feasibility.

翻译：本研究提出了一种优化框架，用于在工业恢复场景中设计自动化机器人与远程操作机器人之间的高效运动优先级。尽管机器人近年来在工业现场日益普及，但在实现人机协作/协同（HRC）方面仍面临挑战——人类工人与机器人在共享工作空间中共同执行协作任务。例如，当工业机器人在工作空间内掉落装配零件时，为确保安全必须暂停相应工厂单元的运行。随后需允许人类工人进入机器人工作空间处理机器人恢复问题。这种流程会导致非连续生产，进而降低生产效率。近年来，机器人远程操作技术作为一种能够使人类远程安全执行任务的新兴解决方案，可应用于制造故障场景中的恢复流程。我们的方案涉及设计一种适当的优先级函数，该函数有助于避免制造机器人与恢复机器人之间的碰撞，并在可接受风险水平下通过最小化生产损失促进连续作业。本文提出一个包含HRC模拟器与优化公式的框架，用于寻找该优先级函数的最优参数。通过定量与定性实验，我们验证了新概念的可行性并展示了其实际应用价值。