The role of operator style on mission energy requirements for tele-operated unmanned ground vehicles

Sadrpour, Amir; Jin, Judy; Ulsoy, A. Galip

doi:10.1109/acc.2014.6859089

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Through the analysis of the robot speed adjustment instructions (actions) output by the trained PSA model for each subject, it was found that the PSA method had been individually adjusted according to the mental state of different subjects. Referring to previous studies on the classification of operators (or drivers) style (Sadrpour et al, 2014;Gilman et al, 2015;Wang et al, 2020), we divided the subjects into aggressive and conservative types according to the speed of the robot controlled. Subject 1 and 5 belonged to the aggressive style, and the other 4 subjects belonged to the conservative style.…”

Section: Personalized Analysis Of Personalized Speed Adaptationmentioning

confidence: 99%

“…In related fields such as teleoperated robots and car driving, the driving styles of operators (or drivers) are mostly divided into conservative and aggressive types according to driving habits and the speed of the robot (or car) being driven (Sadrpour et al, 2014;Gilman et al, 2015;Wang et al, 2020). Referring to research findings in related fields, we divided the subjects into aggressive and conservative types according to the speed of the robot controlled.…”

Section: Limitations and Future Workmentioning

confidence: 99%

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Feasibility study of personalized speed adaptation method based on mental state for teleoperated robots

Zhang

et al. 2022

Front. Neurosci.

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The teleoperated robotic system can support humans to complete tasks in high-risk, high-precision and difficult special environments. Because this kind of special working environment is easy to cause stress, high mental workload, fatigue and other mental states of the operator, which will reduce the quality of operation and even cause safety accidents, so the mental state of the people in this system has received extensive attention. However, the existence of individual differences and mental state diversity is often ignored, so that most of the existing adjustment strategy is out of a match between mental state and adaptive decision, which cannot effectively improve operational quality and safety. Therefore, a personalized speed adaptation (PSA) method based on policy gradient reinforcement learning was proposed in this paper. It can use electroencephalogram and electro-oculogram to accurately perceive the operator’s mental state, and adjust the speed of the robot individually according to the mental state of different operators, in order to perform teleoperation tasks efficiently and safely. The experimental results showed that the PSA method learns the mapping between the mental state and the robot’s speed regulation action by means of rewards and punishments, and can adjust the speed of the robot individually according to the mental state of different operators, thereby improving the operating quality of the system. And the feasibility and superiority of this method were proved. It is worth noting that the PSA method was validated on 6 real subjects rather than a simulation model. To the best of our knowledge, the PSA method is the first implementation of online reinforcement learning control of teleoperated robots involving human subjects.

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Section: Personalized Analysis Of Personalized Speed Adaptationmentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 99%