Enhancing the Quality of Human-Robot Cooperation Through the Optimization of Human Well-Being and Productivity

Abstract

In human-robot interaction frameworks maximizing the team efficiency is crucial. However, it is also essential to mitigate the physical and cognitive workload experienced by the shop-floor worker during the collaborative task. In this chapter we first investigate the impact of the robot interaction role (whether being leader or follower during cooperation) on both the human physiological stress and production rate. Based on that, a game-theoretic approach is proposed to model the trade-off between the maximization of the human performance and the minimization of the human cognitive stress. Then, we describe a closed-loop robot control strategy that, based on the proposed game-theoretic model, enables the robot to simultaneously minimize the human cognitive stress and maximize his/her performance during cooperation, by adjusting its role. Eventually, a real-time task allocation strategy is proposed to both ensure the minimization of the human physical fatigue and the effectiveness of the production process. This method relies on a new sophisticated musculoskeletal model of the human upper-body. All these methodologies have been experimentally tested in realistic human-robot collaborative scenarios involving several volunteers and the ABB IRB 14000 dual-arm “YuMi" collaborative robot.

What Affects Human Decision Making in Human–Robot Collaboration?: A Scoping Review

“…As illustrated in Figure 5, among all the studies, most of the interaction type between humans and robots is collaboration (n = 34, 72.9%), the others are cooperation (n = 8, 16.7%), coexistence (n = 3, 6.2%), and teleoperation (n = 2, 4.2%). Figure 5 shows that the studies reviewed primarily focused on assembly tasks [38,39,48,53,54,57,58,61,63,64,72,73,76,77,80] with other frequent activities including pickand-place [44,55,60,65,79], screwing [41,70,71], handling [38,41], sorting [51,79], lifting [41,49], transferring [36,70], and surgical procedures [47,69]. These tasks represent the core applications of collaborative robots in current research.…”

“…Other unique cobot types included a 4-DoF cobot, the surgical robot da Vinci, and virtual robots in simulated environments, indicating a diverse range of robotic platforms used in HRC research. Figure 5 shows that the studies reviewed primarily focused on assembly tasks [38,39,48,53,54,57,58,61,63,64,72,73,76,77,80] with other frequent activities including pick-and-place [44,55,60,65,79], screwing [41,70,71], handling [38,41], sorting [51,79], lifting [41,49], transferring [36,70], and surgical procedures [47,69]. These tasks represent the core applications of collaborative robots in current research.…”

“…trust [17,45,48,57,73,77] physical attribute of the robot [39,70,72] user interface [37,38,42,46,51,60,63,64,73] dynamic of the situation [75] acceptance [17,57,71,77] robot errors [36,55] control modality [37,65,71] task complexity [36,55,66,69,80] human characteristics [39] trajectory and movement [53,55,78] feedback [39,48,51,52,62,72] design of workspace [39,73,77] physical workload [50] role of the robot [61,76,…”

“…Hostettler et al, suggested adapting the speed of the robot to individual users [78]. In addition, adjusting the robot's role in real-time based on cognitive workload was discussed in several studies [76,80].…”