Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T15:51:41.021Z Has data issue: false hasContentIssue false
  • English
  • Français

Kinematic Design Optimization of an Eight Degree-of-Freedom Upper-Limb Exoskeleton

Published online by Cambridge University Press:  25 June 2019

Amin Zeiaee Open the ORCID record for Amin Zeiaee [Opens in a new window] ,
Rana Soltani-Zarrin ,
Reza Langari  and
Reza Tafreshi
Amin Zeiaee*
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA. E-mails: rana.soltani@tamu.edu, rlangari@tamu.edu
Rana Soltani-Zarrin
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA. E-mails: rana.soltani@tamu.edu, rlangari@tamu.edu
Reza Langari
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA. E-mails: rana.soltani@tamu.edu, rlangari@tamu.edu
Reza Tafreshi
Affiliation:
Department of Mechanical Engineering, Texas A&M University at Qatar, Doha, Qatar. E-mail: reza.tafreshi@qatar.tamu.edu
*
* Corresponding author. E-mail: amin.zeiaee@tamu.edu
Get access Rights & Permissions [Opens in a new window]

Summary

This paper studies the problem of optimizing the kinematic structure of an eight degree-of-freedom upper-limb rehabilitation exoskeleton. The objective of optimization is achieving minimum volume and maximum dexterity in the workspace of daily activities specified by a set of upper-arm configurations. To formulate the problem, a new index is proposed for effective characterization of kinematic dexterity for wearable robots. Additionally, a set of constraints are defined to ensure that the optimal design can cover the desired workspace of the exoskeleton, while singular configurations and physical interferences are avoided. The formulated multi-objective optimization problem is solved using an evolutionary algorithm (Non-dominated Sorting Genetic Algorithm II) and the weighted sum approach. Among the resulted optimal points, the point with least sensitivity with respect to the variations of design variables is chosen as the final design.

Keywords

ExoskeletonsDesign optimizationKinematic designKinematic dexterityWorkspace maximization
Type
Articles
Information
Robotica , Volume 37 , Special Issue 12: Wearable Robotics: Dynamics, Control and Applications , December 2019 , pp. 2073 - 2086
Copyright
© Cambridge University Press 2019 

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

Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A. and Leonhardt, S., “A survey on robotic devices for upper limb rehabilitation,” J. Neuroeng. Rehab. 11, 3 (2014).10.1186/1743-0003-11-3CrossRefGoogle Scholar
Nef, T., Guidali, M. and Riener, R., “ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation,” Appl. Bion. Biomechan. 6, 127–142 (2009).10.1155/2009/962956CrossRefGoogle Scholar
Brewer, B. R., McDowell, S. K. and Worthen-Chaudhari, L. C., “Poststroke upper extremity rehabilitation: A review of robotic systems and clinical results,” Top. Stroke Rehab. 14, 22–44 (2007).10.1310/tsr1406-22CrossRefGoogle Scholar
Lo, H. S. and Xie, S. Q., “Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects,” Med. Eng. Phys. 34, 261–268 (2012).10.1016/j.medengphy.2011.10.004CrossRefGoogle Scholar
Perry, J. C., Rosen, J. and Burns, S., “Upper-limb powered exoskeleton design,” IEEE/ASME Trans. Mechatron . 12, 408–417 (2007).CrossRefGoogle Scholar
Mao, Y. and Agrawal, S. K., “Design of a cable-driven arm exoskeleton (CAREX) for neural rehabilitation,” IEEE Trans. Robot . 28, 922–931 (2012).CrossRefGoogle Scholar
Agarwal, P., Narayanan, M. S., Lee, L., Mendel, F. and Krovi, V. N., “Simulation-based design of exoskeletons using musculoskeletal analysis,” Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, Canada, 1357–1364 (2010).CrossRefGoogle Scholar
Kurtz, R. and Hayward, V., “Multiple-goal kinematic optimization of a parallel spherical mechanism with actuator redundancy,” IEEE Trans. Robot. Automat. 8, 644–651 (1992).CrossRefGoogle Scholar
Stock, M. and Miller, K., “Optimal kinematic design of spatial parallel manipulators: Application to linear delta robot,” J. Mech. Design 125, 292–301 (2003).10.1115/1.1563632CrossRefGoogle Scholar
Gosselin, C. and Angeles, J., “A global performance index for the kinematic optimization of robotic manipulators,” J. Mech. Design 113, 220–226 (1991).CrossRefGoogle Scholar
Zhang, P., Yao, Z. and Du, Z., “Global performance index system for kinematic optimization of robotic mechanism,” J. Mech. Design 136, 031001 (2014).CrossRefGoogle Scholar
Huang, T., Li, M., Li, Z., Chetwynd, D. G. and Whitehouse, D. J., “Optimal kinematic design of 2-DOF parallel manipulators with well-shaped workspace bounded by a specified conditioning index,” IEEE Trans.Robot. Automat . 20, 538–543 (2004).CrossRefGoogle Scholar
Lum, M. J., Rosen, J., Sinanan, M. N. and Hannaford, B., “Kinematic Optimization of a Spherical Mechanism for a Minimally Invasive Surgical Robot,” Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, Louisiana, USA (2004) pp. 829–834.Google Scholar
Faraz, A. and Payandeh, S., “A Robotic Case Study: Optimal Design for Laparoscopic Positioning Stands,” Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico, USA (1997) pp. 1553–1560.Google Scholar
Merlet, J.-P., “Jacobian, manipulability, condition number, and accuracy of parallel robots,” J. Mech. Design 128, 199–206 (2006).CrossRefGoogle Scholar
Hong, K. S. and Kim, J. G., “Manipulability analysis of a parallel machine tool: Application to optimal link length design,” J. Robot. Syst. 17, 403–415 (2000).3.0.CO;2-J>CrossRefGoogle Scholar
Kim, J.-O. and Khosla, P. K., “Design of Space Shuttle Tile Servicing Robot: An Application of Task Based Kinematic Design,” Proceedings of the IEEE International Conference on Robotics and Automation, Atlanta, Georgia, USA (1993) pp. 867–874.Google Scholar
Kim, J.-O. and Khosla, K., “Dexterity Measures for Design and Control of Manipulators,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, Japan (1991) pp. 758–763.Google Scholar
Roberts, R. G., Siddiqui, S. A. and Maciejewski, A. A., “Designing Equally Fault-Tolerant Configurations for Kinematically Redundant Manipulators,” Proceedings of the 41st Southeastern Symposium on System Theory, Tullahoma, Tennessee, USA (2009) pp. 335–339.Google Scholar
Kim, J.-O. and Khosla, P., “A multi-population genetic algorithm and its application to design of manipulators,” Proceedings of the IEEE International Conference of Intelligent Robots and Systems, Raleigh, North Carolina, USA (1992) pp. 279–286.Google Scholar
Khatami, S. and Sassani, F., “Isotropic Design Optimization of Robotic Manipulators Using a Genetic Algorithm Method,” Proceedings of the IEEE International Symposium on Intelligent Control, Vancouver, Canada (2002) pp. 562–567.Google Scholar
Tolani, D., Goswami, A. and Badler, N. I., “Real-time inverse kinematics techniques for anthropomorphic limbs,” Graph. Models 62, 353–388 (2000).10.1006/gmod.2000.0528CrossRefGoogle Scholar
Durmuş, B., Temurtaş, H. and Gün, A., “An Inverse Kinematics Solution Using Particle Swarm Optimization,” 6th International Advanced Technologies Symposium IATS, Elazig, Turkey (2011).Google Scholar
Daya, B., Khawandi, S. and Akoum, M., “Applying neural network architecture for inverse kinematics problem in robotics,” J. Software Eng. Appl. 3, 230 (2010).10.4236/jsea.2010.33028CrossRefGoogle Scholar
Zhang, X. and Nelson, C. A., “Multiple-criteria kinematic optimization for the design of spherical serial mechanisms using genetic algorithms,” J. Mech. Design 133, 011005 (2011).CrossRefGoogle Scholar
Zhang, D. and Gao, Z., “Forward kinematics, performance analysis, and multi-objective optimization of a bio-inspired parallel manipulator,” Robot. Comput.-Integrated Manuf. 28, 484–492 (2012).Google Scholar
Cui, X.,Chen, W., Jin, X. and Agrawal, S. K., “Design of a 7-DOF cable-driven arm exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance,” IEEE/ASME Trans. Mechatron . 22, 161–172 (2017).CrossRefGoogle Scholar
Iqbal, J., Tsagarakis, N. and Caldwell, D., “Design Optimization of a Hand Exoskeleton Rehabilitation Device,” Proceedings of RSS Workshop on Understanding the Human Hand for Advancing Robotic Manipulation, Seattle, Washington, USA (2009) pp. 44–45.Google Scholar
Nakagawara, S., Kajimoto, H., Kawakami, N., Tachi, S., and Kawabuchi, I., “An Encounter-Type Multi-Fingered Master Hand Using Circuitous Joints,” Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain (2005) pp. 2667–2672.Google Scholar
Sledd, A. and O’Malley, M. K., “Performance Enhancement of a Haptic Arm Exoskeleton,” Proceedings of the 14th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Arlington, Virginia, USA (2006) pp. 375–381.Google Scholar
Bian, H., Chen, Z., Wang, H. and Zhao, T., “Mechanical Design of EFW Exo II: A Hybrid Exoskeleton for Elbow-Forearm-Wrist Rehabilitation,” Proceedings of the IEEE International Conference on Rehabilitation Robotics, London, England (2017) pp. 689–694.Google Scholar
Wu, T.-M., Wang, S.-Y. and Chen, D.-Z., “Design of an exoskeleton for strengthening the upper limb muscle for overextension injury prevention,” Mech. Mach. Theory 46, 1825–1839 (2011).CrossRefGoogle Scholar
Lo, H. S. and Xie, S. S., “An Upper Limb Exoskeleton with an Optimized 4R Spherical Wrist Mechanism for the Shoulder Joint,” Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Besancon, France (2014) pp. 269–274.Google Scholar
Gopura, R. A. R. C., Kiguchi, K. and Li, Y., “SUEFUL-7: A 7DOF Upper-Limb Exoskeleton Robot with Muscle-Model-Oriented EMG-Based Control,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA (2009) pp. 1126–1131.Google Scholar
Lo, H. S. and Xie, S., “Optimization and analysis of a redundant 4R spherical wrist mechanism for a shoulder exoskeleton,” Robotica 32, 1191–1211 (2014).CrossRefGoogle Scholar
Jamwal, P. K., Xie, S. and Aw, K. C., “Kinematic design optimization of a parallel ankle rehabilitation robot using modified genetic algorithm,” Robot. Autonom. Syst. 57, 1018–1027 (2009).Google Scholar
Zeiaee, A., Soltani-Zarrin, R., Langari, R. and Tafreshi, R., “Design and Kinematic Analysis of a Novel Upper Limb Exoskeleton for Rehabilitation of Stroke Patients,” Proceedings of the 2017 International Conference on Rehabilitation Robotics, London, England (2017) pp. 759–764.Google Scholar
He, J. H. X. T. J., “Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies,” IEEE Trans. Syst. Man Cybernet. 46, 926–935 (2016).Google Scholar
Chadwick, E. K., Blana, D., Kirsch, R. F. and Van Den Bogert, A. J., “Real-time simulation of threedimensional shoulder girdle and arm dynamics,” IEEE Trans. Biomed. Eng. 61, 1947–1956 (2014).CrossRefGoogle Scholar
Klopčar, N. and Lenarčič, J., “Bilateral and unilateral shoulder girdle kinematics during humeral elevation,” Clin. Biomech. 21, S20–S26 (2006).CrossRefGoogle Scholar
Inman, V. T. and Abbott, L. C., “Observations on the function of the shoulder joint,” J. Bone Joint Surg. Am. 26, 1–30, (1944).Google Scholar
Soltani-Zarrin, R., Zeiaee, A., Langari, R. and Robson, N., “Reference Path Generation For Upper-Arm Exoskeletons Considering Scapulohumeral Rhythms,” Proceedings of the 2017 International Conference on Rehabilitation Robotics, London, England (2017) pp. 753–758.Google Scholar
Kiguchi, K., Iwami, K., Yasuda, M., Watanabe, K. and Fukuda, T., “An exoskeletal robot for human shoulder joint motion assist,” IEEE/ASME Trans. Mechatron . 8, 125–135 (2003).10.1109/TMECH.2003.809168CrossRefGoogle Scholar
Ren, Y., Kang, S. H., Park, H., Wu, Y. and Zhang, L., “Developing a multi-joint upper limb exoskeleton robot for diagnosis, therapy, and outcome evaluation in neurorehabilitation,” IEEE Trans. Neural Syst. Rehab. Eng. 21, 490–499 (2013).Google Scholar
Riener, R., Nef, T. and Colombo, G., “Robot-aided neurorehabilitation of the upper extremities,” Med. Biol. Eng. Comput. 43, 2–10 (2005).CrossRefGoogle Scholar
Soltani-Zarrin, R., Zeiaee, A., Langari, R., “Human-like path generation in upper-limb exoskeleton”, arXiv preprint.Google Scholar
Gunasekara, J., Gopura, R., Jayawardena, T., and Mann, G., “Dexterity Measure of Upper Limb Exoskeleton Robot with Improved Redundancy,” Proceedings of the IEEE International Conference on Industrial and Information Systems, Sri Lanka (2013) pp. 548–553.Google Scholar
Kucuk, S., “Kinematics, Singularity and Dexterity Analysis of Planar Parallel Manipulators based on DH Method,” In: Robot Manipulators New Achievements, Lazinica, Aleksandar and Kawai, Hiroyuki (Ed.), ISBN: 978-953-307-090-2, InTech.Google Scholar
Soltani-Zarrin, R., Zeiaee, A., Langari, R. and Tafreshi, R., “A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms,” Proceedings of the International Symposium on Wearable Robotics and Rehabilitation, Houston, Texas, USA (2017) pp. 1–2.Google Scholar
Anderson-Cook, C. M., Practical Genetic Algorithms, Taylor & Francis (2005).Google Scholar
Goldberg, D. E., Genetic Algorithms Pearson Education India (2006).Google Scholar
Nocedal, J. and Wright, S. J., “Sequential quadratic programming,” Springer (2006).Google Scholar
Boggs, P. T. and Tolle, J. W., “Sequential quadratic programming,” Acta Num . 4, 1–51 (1995).CrossRefGoogle Scholar