Hostname: page-component-76fb5796d-skm99 Total loading time: 0 Render date: 2024-04-26T00:56:08.963Z Has data issue: false hasContentIssue false
  • English
  • Français

Gait multi-objectives optimization of lower limb exoskeleton robot based on BSO-EOLLFF algorithm

Published online by Cambridge University Press:  25 August 2022

Peng Zhang ,
Junxia Zhang Open the ORCID record for Junxia Zhang [Opens in a new window]  and
Ahmed Elsabbagh
Peng Zhang
Affiliation:
Tianjin University of Science and Technology, Tianjin, China Tianjin Key Laboratory for Integrated Design and Online Monitor Center of Light Design and Food Engineering Machinery Equipment, Tianjin, China
Junxia Zhang*
Affiliation:
Tianjin University of Science and Technology, Tianjin, China Tianjin Key Laboratory for Integrated Design and Online Monitor Center of Light Design and Food Engineering Machinery Equipment, Tianjin, China
Ahmed Elsabbagh
Affiliation:
School of Design and Production Engineering, Ain Shams University, Cairo, Egypt
*
*Corresponding author. E-mail: zjx@tust.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Aiming at problems of low optimization accuracy and slow convergence speed in the gait optimization algorithm of lower limb exoskeleton robot, a novel gait multi-objectives optimization strategy based on beetle swarm optimization (BSO)-elite opposition-based learning (EOL) levy flight foraging (LFF) algorithm was proposed. In order to avoid the algorithm from falling into the local optimum, the EOL strategy with global search capability, the LFF strategy with local search capability and the dynamic mutation strategy with high population diversity were introduced to improve optimization performance. The optimization was performed by establishing a multi-objectives optimization function with the robot’s gait zero moment point (ZMP) stability margin and driving energy consumption. The joint comparative tests were carried out in SolidWorks, ADAMS and MATLAB software. The simulation results showed that compared with the particle swarm optimization algorithm and the BSO algorithm, the ZMP stability margin obtained by the BSO-EOLLFF algorithm was increased, and the average driving energy consumption was reduced by 25.82% and 17.26%, respectively. The human-machine experiments were conducted to verify the effectiveness and superiority. The robot could realize stable and smooth walking with less energy consumption. This research will provide support for the application of exoskeleton robot.

Keywords

exoskeleton robotmulti-objectives optimization functionzero moment pointBSO-EOLLFF algorithm
Type
Research Article
Information
Robotica , Volume 41 , Issue 1 , January 2023 , pp. 174 - 192
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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

Li, Y., Wei, Q., Gou, W., et al., “Effects of mirror therapy on walking ability, balance and lower limb motor recovery after stroke: A systematic review and meta-analysis of randomized controlled trials,” Clin. Rehabil. 32(8), 1007–1021 (2018).CrossRefGoogle ScholarPubMed
Beisheim, E. H., Horne, J. R., Pohlig, R., et al., T., “Differences in measures of strength and dynamic balance among individuals with lower-Limb loss classified as functional level K3 versus K4,” Am. J. Phys. Med. Rehab. 98(9), 1–10 (2019).CrossRefGoogle ScholarPubMed
Zou, Y., Ma, H. and Han, Z., “Force control of wire driving lower limb rehabilitation robot,” Technol. Health Care 26(11), 1–10 (2018).CrossRefGoogle ScholarPubMed
Esquenazi, A., Talaty, M., Packel, A., et al., “The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury,” Am. J. Phys. Med. Rehab. 91(11), 911–921 (2012).CrossRefGoogle ScholarPubMed
Mummolo, C., Peng, W. Z., Agarwal, S., Griffin, R., Neuhaus, P. D. and Kim, J. H., “Stability of mina v2 for robot-assisted balance and locomotion,” Front. Neurorobot. 12, 62 (2018).CrossRefGoogle ScholarPubMed
Wang, S., Wang, L., Meijneke, C., et al., “Design and control of the MINDWALKER exoskeleton,” IEEE Trans. Neur. Syst. Reh. 23, 277–286 (2014).CrossRefGoogle ScholarPubMed
Gasser, B. W., Bennett, D. A., Durrough, C. M., et al., , “Design and Preliminary Assessment of Vanderbilt Hand Exoskeleton,” In: 2017 International Conference on Rehabilitation Robotics (ICORR) (IEEE, 2017) pp. 1537–1542.CrossRefGoogle Scholar
Yang, M., Wang, X., Zhu, Z., Xi, R. and Wu, Q., “Development and control of a robotic lower limb exoskeleton for paraplegic patients,” Proc. Inst. Mech. Eng. 233, 1989–1996 (2019).Google Scholar
Zhang, X., Qin, K. and Shi, Y., “Review of human exoskeleton suit technology,” Comput. Sci 42, 1–7 (2015).Google Scholar
Vukobratović, M. and Borovac, B., “Zero-Moment Point,” In: Thirty Five Years of Its Life vol. 1 (2004) pp. 157–173.Google Scholar
Liu, X., Low, K. H. and Yu, H., “Development of A Lower Extremity Exoskeleton for Human Performance Enhancement,” In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2004) pp. 3889–3894.Google Scholar
Low, K. H., Liu, X. and Yu, H., “Development of NTU wearable exoskeleton system for assistive technologies,” In: IEEE International Conference on Mechatronics and Automation vol. 2 (IEEE, 2005) pp. 1099–1106.Google Scholar
Liu, X. and K.H. Low, "Development and Preliminary Study of the NTU Lower Extremity Exoskeleton,” In: 2004 IEEE Conference on Cybernetics and Intelligent Systems (IEEE, 2004) pp. 1243–1247.Google Scholar
Low, K. H., Liu, X., Goh, C. H., et al., “Locomotive control of a wearable lower exoskeleton for walking enhancement,” J. Vib. Control 12(12), 1311–1336 (2006).CrossRefGoogle Scholar
WuC., C. W. Wu, W., Dynamic Stability Analysis and Gait Tracking Control for the Lower Extremity Exoskeleton” , M.S. Thesis, (Central South University, Hunan, China, 2014).Google Scholar
Feng, S., Whitman, E., Xinjilefu, X. and Atkeson, C. G., “Optimization-based full body control for the DARPA robotics challenge,” J. Field Robot 32(2), 293–312 (2015).CrossRefGoogle Scholar
Gasparrigm, G. M., Manaras, S. and Caporaled, D., “Efficient walking gait generation via principal component representation of optimal trajectories: Application to a planar biped robot with elastic joints,” IEEE Robot. Automat. Lett. 3, 2299–2306(2018).Google Scholar
Ang, Z., Xiaojing, M., Jiayong, Z., et al., “Integrated design and finite element analysis of the upper limb exoskeleton assist system,” Modern Manufact. Eng. 5, 59–63 (2020).Google Scholar
Juan, L., Xiaowei, C., Weida, L., et al., “Static and stable gait planning method of lower limb exoskeleton walking-assisting robot based on center of gravity stability constraint,” Mech. Electr. Eng. 34, 1075–1080 (2017).Google Scholar
Sreenath, K., Park, H.-W. and Poulakakis, I., “Embedding active force control within the compliant hybrid zero dynamics to achieve stable, fast running on MABEL,” Int. J. Robot. Res. 32(3), 324–345 (2013.CrossRefGoogle Scholar
Zhang, P. and Zhang, J., “Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm,” Robotica 40(8), 1–16 (2022). doi: 10.1017/S0263574721001922.CrossRefGoogle Scholar
Jiang, X. and Li, S., “Beetle antennae search without parameter tuning (BAS-WPT) for multi-objectives optimization,” Neural Evol. Comput. 27 , 78–86(2019), Available: ar Xiv: 1711. 02395.Google Scholar
Liu, Y., Qian, Z. and Jia, D., “Universal localization algorithm based on beetle antennae search in indoor environment,” J. Electron. Inform. Technol. 41, 1565–1571 (2019).Google Scholar
Wu, Q., Ma, Z., Xu, G., Li, S. and Chen, D., “A novel neural network classifier using beetle antennae search algorithm for pattern classification,” IEEE Access 7, 64686–64696 (2019).CrossRefGoogle Scholar
Tizhoosh, H. R., “Opposition-based Learning: A New Scheme for Machine Intelligence,” In: Proceedings of International Conference on Computational Intelligence for Modeling Control and Automation (USA: IEEE, 2005) pp. 695–701.Google Scholar
Zhou, X.-Y., Wu, Z.-J., Wang, H., et al., “Elite opposition-based particle swarm optimization,” Acta Electron. Sin. 41, 1647–1652 (2013).Google Scholar
Jiadong, R., Xinqian, L., Qian, W. Haitao, H. and Xiaolin, Z.., “An multi-level intrusion detecti on method based on KNN outlier detection and random forests,” J. Comput. Res. Develop. 56, 566–575 (2019).Google Scholar
Ni, G., Ling, G., He, Y.-Y. and Hai, W., “A light weight intrusi on detection model based on auto encoder network with feature reduction,” Acta Electron. Sin. 45, 730–739 (2017).Google Scholar
Rudolph, G., “Local convergence rates of simple evolutionary algorithms with Cauchy mutations,” IEEE Trans. Evolut. Comput. 4(4), 249–258 (1997).CrossRefGoogle Scholar
Maharaj, J. N., et al., “Modelling the complexity of the foot and ankle during human locomotion: The development and validation of a multi-segment foot model using biplanar videoradiography,” Comput. Method Biomech. Biomed. Eng. 25(5), pp:554–565 (2022).CrossRefGoogle ScholarPubMed
Killinger, B., et al., “The effects of blood flow restriction on muscle activation and hypoxia in individuals with chronic ankle instability,” J. Sport Rehabil. 29(5), pp:633–639 (2019).CrossRefGoogle ScholarPubMed
Sato, N., et al., “Effect of individual ankle taping components on the restriction of ankle external inversion moment,” Sport Biomech. 37, 1–11 (2020).CrossRefGoogle Scholar
Mihcin, S., “Simultaneous validation of wearable motion capture system for lower body applications: Over single plane range of motion (ROM) and gait activities,” Biomed. Eng./Biomedizinische Technik 67(3), 185–199 (2022). doi: 10.1515/bmt-2021-0429.CrossRefGoogle ScholarPubMed
Mihcin, S., Ciklacandir, S., Kocak, M. and Tosun, A., “Wearable motion capture system evaluation for biomechanical studies for hip joints,” ASME. J. Biomech. Eng. 143(4), 044504 (2021). doi: 10.1115/1.4049199.CrossRefGoogle ScholarPubMed
Iosa, M., Cereatti, A., Merlo, A., Campanini, I., Paolucci, S. and Cappozzo, A., “Assessment of waveform similarity in clinical gait data: the linear fit method,” BioMed Res. Int., 2014, Article ID 214156 , 7 pages (2014). doi: 10.1155/2014/214156.Google ScholarPubMed