From Raw Signals to Human Skills Level in Physical Human-Robot Collaboration for Advanced-Manufacturing Applications

Blanchet, Katleen; Kchir, Selma; Bouzeghoub, Amel; Lebec, Olivier; Hède, Patrick

doi:10.1007/978-3-030-36711-4_47

Cited by 3 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recognizing that novice and expert operator statuses impact system performance and cobot utilization (Blanchet et al, 2019), the effects of prior operator experience, task familiarization, and training methods on HRC are understudied. For example, 70.49% of included papers in the review failed to mention the training methods.…”

Section: Opportunities For Future Workmentioning

confidence: 99%

Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review

2022

View full text Add to dashboard Cite

The degree of successful human-robot collaboration is dependent on the joint consideration of robot factors (RF) and human factors (HF). Depending on the state of the operator, a change in a robot factor, such as the behavior or level of autonomy, can be perceived differently and affect how the operator chooses to interact with and utilize the robot. This interaction can affect system performance and safety in dynamic ways. The theory of human factors in human-automation interaction has long been studied; however, the formal investigation of these HFs in shared space human-robot collaboration (HRC) and the potential interactive effects between covariate HFs (HF-HF) and HF-RF in shared space collaborative robotics requires additional investigation. Furthermore, methodological applications to measure or manipulate these factors can provide insights into contextual effects and potential for improved measurement techniques. As such, a systematic literature review was performed to evaluate the most frequently addressed operator HF states in shared space HRC, the methods used to quantify these states, and the implications of the states on HRC. The three most frequently measured states are: trust, cognitive workload, and anxiety, with subjective questionnaires universally the most common method to quantify operator states, excluding fatigue where electromyography is more common. Furthermore, the majority of included studies evaluate the effect of manipulating RFs on HFs, but few explain the effect of the HFs on system attributes or performance. For those that provided this information, HFs have been shown to impact system efficiency and response time, collaborative performance and quality of work, and operator utilization strategy.

show abstract

Section: Opportunities For Future Workmentioning

confidence: 99%

Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review

2022

View full text Add to dashboard Cite

show abstract

“…Classical approaches to safety do not consider the contextual dynamics which appear in the work area. This limitation, which may have a negative effect on the efficiency of the process, can be removed if robots could understand the situation in the environment and could predict dangerous situations (Blanchet et al, 2018). In this way, we can also reduce the safety risk for humans (Michalos et al, 2015).…”

Section: Situation Awarenessmentioning

confidence: 99%

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

Zorman

Žlahtič

Stradovnik

et al. 2022

View full text Add to dashboard Cite

PurposeCollaborative robotics and autonomous driving are fairly new disciplines, still with a long way to go to achieve goals, set by the research community, manufacturers and users. For technologies like collaborative robotics and autonomous driving, which focus on closing the gap between humans and machines, the physical, psychological and emotional needs of human individuals becoming increasingly important in order to ensure effective and safe human–machine interaction. The authors' goal was to conceptualize ways to combine experience from both fields and transfer artificial intelligence knowledge from one to another. By identifying transferable meta-knowledge, the authors will increase quality of artificial intelligence applications and raise safety and contextual awareness for users and environment in both fields.Design/methodology/approachFirst, the authors presented autonomous driving and collaborative robotics and autonomous driving and collaborative robotics' connection to artificial intelligence. The authors continued with advantages and challenges of both fields and identified potential topics for transferrable practices. Topics were divided into three time slots according to expected research timeline.FindingsThe identified research opportunities seem manageable in the presented timeline. The authors' expectation was that autonomous driving and collaborative robotics will start moving closer in the following years and even merging in some areas like driverless and humanless transport and logistics.Originality/valueThe authors' findings confirm the latest trends in autonomous driving and collaborative robotics and expand them into new research and collaboration opportunities for the next few years. The authors' research proposal focuses on those that should have the most positive impact to safety, complement, optimize and evolve human capabilities and increase productivity in line with social expectations. Transferring meta-knowledge between fields will increase progress and, in some cases, cut some shortcuts in achieving the aforementioned goals.

show abstract

How to Guide Humans Towards Skills Improvement in Physical Human-Robot Collaboration Using Reinforcement Learning?

Blanchet

Bouzeghoub

Kchir

et al. 2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

From Raw Signals to Human Skills Level in Physical Human-Robot Collaboration for Advanced-Manufacturing Applications

Cited by 3 publications

References 15 publications

Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review

Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review

Transferring artificial intelligence practices between collaborative robotics and autonomous driving

How to Guide Humans Towards Skills Improvement in Physical Human-Robot Collaboration Using Reinforcement Learning?

Contact Info

Product

Resources

About