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 ( CC ) 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 CC ). If a system could identify the human-work area, designate it as a no-entry space in CC for the robot ( CCPP ), and dynamically set the closed CC ( CCCC ) with shrinking CC by CCPP , productivity would improve thanks to enabling the robot to work in CCCC and safety would be ensured thanks to allowing the human to continue working in CCPP . In this study, we propose a new concept of a dynamic collaborative workspace (DCW) that dynamically sets CCCC and CCPP 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 CC , CC 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.