TY - GEN
T1 - Optimizing Electric Power Efficiency in Power-Assisted Human-Robot Collaborative Manipulation of Objects
AU - Rahman, S. M.Mizanoor
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - A power assist robotic system (PARS) was developed so human users could use it for co-manipulating (co-lifting) heavy objects. User’s weight perceptual cue was considered when deriving system dynamics and control. A computational model to estimate electric power consumption of the robotic system for object lifting tasks was derived, and its effectiveness was experimentally examined. Experimental results proved the effectiveness of the electric power estimation model. The results showed that electric power consumption was linearly related to payloads and robot velocities. The results can be used to design and develop predictive robot control strategies (e.g., MPC-Model Predictive Control) for optimizing electric power consumption, human interactions, and manipulation performance in manipulating large and heavy objects or materials in industries using industrial power assist robotic systems.
AB - A power assist robotic system (PARS) was developed so human users could use it for co-manipulating (co-lifting) heavy objects. User’s weight perceptual cue was considered when deriving system dynamics and control. A computational model to estimate electric power consumption of the robotic system for object lifting tasks was derived, and its effectiveness was experimentally examined. Experimental results proved the effectiveness of the electric power estimation model. The results showed that electric power consumption was linearly related to payloads and robot velocities. The results can be used to design and develop predictive robot control strategies (e.g., MPC-Model Predictive Control) for optimizing electric power consumption, human interactions, and manipulation performance in manipulating large and heavy objects or materials in industries using industrial power assist robotic systems.
UR - http://www.scopus.com/inward/record.url?scp=85177078819&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-47454-5_7
DO - 10.1007/978-3-031-47454-5_7
M3 - Conference contribution
AN - SCOPUS:85177078819
SN - 9783031474538
T3 - Lecture Notes in Networks and Systems
SP - 92
EP - 101
BT - Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th Future Technologies Conference, FTC 2023
Y2 - 2 November 2023 through 3 November 2023
ER -