Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-13T00:42:43.770Z Has data issue: false hasContentIssue false
  • English
  • Français

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]
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Song, D., Zhang, L. X. and Wang, B. J., “The control strategy of flexible cable driven force interactive robot,” Robot 40(2), 440–447 (2018).Google Scholar
Song, D., Zhang, L. X. and Xue, F., “Configuration optimization and a tension distribution algorithm for cable-driven parallel robots,” IEEE Access 6, 33928–33940 (2018).CrossRefGoogle Scholar
Han, J. Z. and Chen, W. H., “Velocity control algorithm in glass polishing based on the cubic NURBS curve,” Proc. Inst. Mech. Eng. Part C-J. Mech. Eng. Sci. 232(4), 685–696 (2018).10.1177/0954406217736080CrossRefGoogle Scholar
Tsai, M. S., Wu, S. K. and Huang, H. W., “Study on Acceleration Deceleration Feedrate Planning for Multi-Block Line Segments Using Estimated Contour Error Formulation,” In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2014) pp. 489–493.Google Scholar
Lin, H. I., “A fast and unified method to find a minimum-Jerk Robot joint trajectory using particle swarm optimization,” J. Intel. & Robot. Syst. 75, 379–392 (2014).CrossRefGoogle Scholar
Lu, T. C. and Chen, S. L., “Genetic Algorithm-Based S-curve Acceleration and Deceleration for Five-Axis Machine Tools,” Int. J. Adv. Manuf. Technol. 87(1), 219–232 (2016).CrossRefGoogle Scholar
Lu, T. C., Chen, S. L. and Yang, E. C., “Near time-optimal S-curve velocity planning for multiple line segments under axis constraints,” IEEE Trans. Ind. Electron. 65(12), 9582–9592 (2018).CrossRefGoogle Scholar
Liang, F. S., Zhao, J. and Ji, S. J., “An iterative feed rate scheduling method with confined high-order constraints in parametric interpolation,” Int. J. Adv. Manuf. Technol. 92(5), 2001–2015 (2017).CrossRefGoogle Scholar
Yuan, M. X., Chen, Z., Yao, B. and Zhu, X. C., “Time optimal contouring control of industrial biaxial gantry: A highly efficient analytical solution of trajectory planning,” IEEE-ASME Trans. Mechatron. 22(1), 247–257 (2017).CrossRefGoogle Scholar
Bharathi, A. and Dong, J. Y., “Feedrate Optimization for Smooth Minimum-Time Trajectory Generation with Higher Order Constraints,” Int. J. Adv. Manuf. Technol. 82, 1029–1040 (2016).CrossRefGoogle Scholar
Fang, C. X., Zhang, H. and Ye, P. Q., “Convex optimization approach in time-optimal feed-rate planning for CNC,” Comput. Integr. Manuf. Syst. 21(1), 187–194 (2015).Google Scholar
Zajac, F. E., “Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control,” Crit. Rev. Biomed. Eng. 17(4), 359–411 (1989).Google ScholarPubMed
Murray, M. and Wendy, D., “Variation of muscle moment arms with elbow and forearm position,” J. Biomech. 28(5), 513–525 (1995).CrossRefGoogle ScholarPubMed
Storace, A. and Wolf, B., “Functional analysis of the role of the finger tendons,” J. Biomech. 12(8), 575–578 (1979).CrossRefGoogle ScholarPubMed
Zhao, J. D., Liu, Y. W., Cai, H. G. and Liu, H., “An Improved Algorithm of Measuring Extravehicular Mobility Unit (EMU) Spacesuit Joint Damping Parameters for the Old Passive Robot System,” In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2008) pp. 77–82.Google Scholar
Katherine, R. S. Holzbaur, Murray, W. M. and Delp, S. L., “A model of the upper extremity for simulating Musculoskeletal surgery and analyzing neuromuscular control,” Ann. Biomed. Eng. 33(6), 829–840 (2005).Google Scholar
Garg, H., “A hybrid PSO-GA algorithm for constrained optimization problems,” Appl. Math. Comput. 274, 292–305 (2016).Google Scholar
Ding, Q., Chen, H., Wang, C., Jiang, A. P. and Lin, W. W., “Research of PSO Algorithm with Variable Constraints in Process System,” In: Proceedings of the 10th World Congress on Intelligent Control and Automation (2012) pp. 544–548.Google Scholar
Gao, G., Sun, C., Zeng, J. and Xue, S. D., “A Constraint Approximation Assisted PSO for Computationally Expensive Constrained Problems,” In: Proceeding of the 11th World Congress on Intelligent Control and Automation (2014) pp. 1354–1359.Google Scholar
Kohler, M., Forero, L., Vellasco, M., Tanscheit, R. and Pacheco, M. A., “PSO Plus: A Nonlinear Constraints-Handling Particle Swarm Optimization,” In: Proceeding of the 2016 IEEE Congress on Evolutionary Computation (2016) pp. 2518–2523.Google Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm NSGA-2,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar
Gionfra, N., Sandou, G., Siguerdidjane, H., Faille, D. and Loevenbruck, P., “Wind farm distributed PSO-based control for constrained power generation maximization,” Renew. Energy 133, 103–117 (2019).10.1016/j.renene.2018.09.084CrossRefGoogle Scholar