Iterative learning of human partner’s desired trajectory for proactive human–robot collaboration

Xia, Jingkang; Huang, Deqing; Li, Yanan; Qin, Na

doi:10.1007/s41315-020-00132-5

Cited by 13 publications

(16 citation statements)

References 39 publications

(57 reference statements)

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“…Exoskeletons can assist the muscles in the body by redistributing or reducing their loads. As an example, motorized or hydraulic actuators that are strategically placed parallel to the quads could reduce muscle fatigue during a squatting motion (Hite 2014;Mooney et al 2014;Zhang et al 2017;Ding et al 2018;Xia et al 2020;Patil et al 2018;Xu et al 2020). An example of a passive exoskeleton system would be elastic bands spanning the length of the quads, acting as an artificial muscle (Herr and Langman 1997;Briner and Linn.…”

Section: Discussionmentioning

confidence: 99%

Pressure monitoring based identification of the EOD suit–human interface load distribution

Deane

Kao

et al. 2021

Int J Intell Robot Appl

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Soldiers working in the field of explosive ordnance disposal (EOD) wear cumbersome personal protective equipment (PPE) that may affect their performance in the field, limit their mobility, and cause discomfort. The objective of this study was to develop a test methodology to identify the relationship between EOD suit interface loads and mission-critical performance metrics. The physical interactions between an EOD suit and human subjects were monitored using a distributed pressure sensor network to investigate the interface load distribution during EOD-related physical positions and activities. More specifically, a Med-Eng brand Model EOD 8 suit was utilized to evaluate shoulder discomfort and leg mobility restriction. Thirty-four college aged adults of varying athletic abilities completed a test course and walked on a treadmill for 2 min while wearing the EOD 8. Demographic information was collected before testing via a survey and qualitative observations were collected at the end of testing with a questionnaire. After each test course repetition, participants ranked their perceived exertion using the Borg scale. Overall, the time it took participants to complete the test course increased by 17% and participants experienced a 60% increase in perceived exertion while wearing the EOD 8. The region that experienced the most pain and discomfort was the top of the shoulders (59%) and there was a negative correlation (r = − 0.5, p < 0.05) between participants' body mass index (BMI) and the max shoulder pressure. The groin protector was found to restrict hip rotation when the subject squatted to pick up an object, producing a pressure 30-times higher than without the EOD 8. These results suggest that a range of motion evaluation method for EOD suits and other protective ensemble can be successfully developed using a combination of user feedback and strategically placed pressure sensors. This study implements the largest pressure region ever recorded on the human body and is the first of its kind to investigate the movement restriction of PPE for various practical tasks.

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Section: Discussionmentioning

confidence: 99%

Pressure monitoring based identification of the EOD suit–human interface load distribution

Deane

Kao

et al. 2021

Int J Intell Robot Appl

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“…In order for robots to serve humans, the medium of information exchange between humans and machines is indispensable. e motion control function is the core of the lower limb exoskeleton rehabilitation robot to achieve different strategies for the rehabilitation mode [22]. erefore, the quality of the motion control directly determines whether the robot can complete tasks efficiently and accurately, as well as the safety and comfort of the user.…”

Section: Basic Principles Of Iterative Learning Controlmentioning

confidence: 99%

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

2021

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At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

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“…Based on ILC, the robot can adapt its trajectory in [26] by minimizing the interaction force error so that it can correctly infer the human intention and efficiently cooperate with the human user. In [27], an iterative learning scheme is developed, with which the robot can learn the desired trajectory of the human user in the presence of uncertain iteration periods. The methods in [26], [27] update the robot's parameters point to point so are inherently limited by the capability to deal with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…In [27], an iterative learning scheme is developed, with which the robot can learn the desired trajectory of the human user in the presence of uncertain iteration periods. The methods in [26], [27] update the robot's parameters point to point so are inherently limited by the capability to deal with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

Maqsood

Huang

Yang

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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Iterative learning of human partner’s desired trajectory for proactive human–robot collaboration

Cited by 13 publications

References 39 publications

Pressure monitoring based identification of the EOD suit–human interface load distribution

Pressure monitoring based identification of the EOD suit–human interface load distribution

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

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