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Velocity Planning for Astronaut Virtual Training Robot with High-Order Dynamic Constraints

Published online by Cambridge University Press:  10 February 2020

Lan Wang ,
Lingjie Lin Open the ORCID record for Lingjie Lin [Opens in a new window] ,
Ying Chang  and
Da Song
Lan Wang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China, E-mails: [email protected], [email protected], [email protected]
Lingjie Lin*
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China, E-mails: [email protected], [email protected], [email protected]
Ying Chang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China, E-mails: [email protected], [email protected], [email protected]
Da Song
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China, E-mails: [email protected], [email protected], [email protected] School of Mechanical Engineering, Northeast Electric Power University, Jilin, 132012, P.R. China
*
*Corresponding author. E-mail: [email protected]
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Summary

In order to improve the training efficiency and establish a multi-person cooperative training simulation system, including “virtual human,” in the process of virtual reality-based astronaut training, it is necessary to plan the velocity at which astronauts carry the target object. A velocity planning algorithm, combining a traditional six-stage acceleration/deceleration algorithm, based on a time-discrete model with high-order dynamic constraints, considering the elastic damping torque of the space suit, is proposed. The described algorithm is verified on MATLAB to prove its feasibility. Compared to other algorithms, the planning time of the proposed algorithm is significantly reduced.

Keywords

Velocity planningAstronaut virtual trainingDynamic constraintParticle swarm optimization
Type
Articles
Information
Robotica , Volume 38 , Issue 12 , December 2020 , pp. 2121 - 2137
Copyright
Copyright © Cambridge University Press 2020

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