Dynamic Collaborative Workspace Based on Human Interference Estimation for Safe and Productive Human-Robot Collaboration

—Collaborative robots that operate safely close to workers without fences have attracted attention, but few examples of such human-robot collaboration (HRC) have been seen in factories. The main reason is the difficulty in balancing safety and productivity. Current fenceless HRC systems stop the robot when a human enters the collaborative workspace (C) where both human and robot can work to ensure safety, which ISO/TS15066 regulates. The robot stops even when the human is far enough away, so productivity is drastically decreased (FCW, Fixed C). If a system could identify the human-work area, designate it as a no-entry space in C for the robot (C^P ), and dynamically set the closed C (C^C) with shrinking C by C^P , productivity would improve thanks to enabling the robot to work in C^C and safety would be ensured thanks to allowing the human to continue working in C^P . In this study, we propose a new concept of a dynamic collaborative workspace (DCW) that dynamically sets C^C and C^P based on the human’s predicted trajectory. It also provides visual and auditory prompts to enable the human to understand DCW states, i.e., when a human enters C, C is changed, and the robot is in emergency mode. We compared four HRC systems using a real robot arm: two conventional FCW ones with and without fences and two proposed DCW ones with and without a state indicator and found that the proposed system with a state indicator has the best productivity and ensures the same level of safety as the conventional system with fences.