Enhancing the quality of human-robot cooperation through the optimization of human stress, safety and productivity
With the advent of Industry 4.0, collaborative robotics has become one of the enabling technologies of the smart factory. The collaborative robots (cobots) embody the most crucial cornerstones of this industrial revolution such as adaptability, flexibility, efficiency, interoperability and human-centered decisions. To improve the effectiveness and the fluency of cooperation, the cobot must be endowed with several advanced capabilities. In particular, it should be able to autonomously select a strategy that ensures the adaptability to the behavioral, cognitive and physical features of the specific human it is cooperating with, while guaranteeing the efficiency of the productive process.
This thesis aims at proposing new applications and strategies that enhance the quality of human-robot interaction (HRI) in industrial frameworks, with a major focus on solving the trade-off between human efficiency and workplace wellbeing.
The current industrial revolution has radically changed the paradigm of the shop-floor worker which is now required to learn and perform different tasks during the same working shift. Making the collaboration between human and robot easier becomes then crucial. To exploit the recent technological developments, a novel holographic mixed-reality (MR) interface has been developed to support the operator during the learning phase of a new collaborative task. Besides, by incorporating the sensing capabilities of a MR headset with the ones of the work-cell vision system, a constrained particle filer-based method has been developed with the aim to improve the perception of the human operator in case of partial occlusion of his/her body. Furthermore, a digital twin (DT) of the collaborative workspace has been exploited both to simulate the robot motion, and to represent the work-cell volume occupied by the human operator. Based on this knowledge, the cognitive unit supervising the work-cell has been enabled to evaluate online the optimal trajectory for the cobot that simultaneously minimizes the risk of collisions as well as the robot cycle time, by leveraging a genetic approach. These researches allowed to increase the human safety and the robot adaption capabilities. The impact of crucial human factors such as cognitive, physical distress and interaction role (whether being leader or follower during cooperation) in influencing the quality and effectiveness of human-robot interaction has been a major focus of this work. In particular, a detailed analysis on how the interaction role of the robot influences the psycho-physiological response and the production rate of the human fellow operator has been carried out. Based on that, a novel method exploiting a game-theoretic approach has been proposed to model the trade-off between the human performance maximization and cognitive stress minimization. Besides, the outcomes of the previous analysis have been pivotal to develop a novel robot adaptive control strategy. More specifically, the robot has been endowed with the capability of applying a suitable alternation of the leader-follower interaction modes, based on the real-time evaluation of human stress and performance, with the aim of simultaneously increasing the human productivity and mitigating his/her cognitive stress. Ultimately, a real-time task allocation strategy guaranteeing the minimization of the human physical fatigue during the execution of the task activities, as well as the effectiveness of the production process, has been developed, by relying on a sophisticated musculoskeletal model of the human upper body.
The effectiveness of the proposed methods has been experimentally validated in realistic human-robot collaborative scenarios involving several volunteers.
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