A Control Method of Grasping Force for Soft Exoskeleton Hand
LIU Ziwen1,2, ZHAO Liang2, YU Peng2, YANG Tie2, YANG Yang2, CHANG Junling3, ZHAO Xingang2, LIU Lianqing2
1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2. The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
3. Liaoning Provincial Rehabilitation Center for the Disabled, Shenyang 110015, China
Abstract:In order to assist the patients without the hand movement function to grasp the daily necessities, a soft exoskeleton robot system based on wire tension feedback is developed, which can realize the stable control of the fingertip force. Firstly, the structural design and control strategy of the soft exoskeleton glove are introduced. Then a static mechanics model of the finger is established, and the contact force between the fingertip and the object is calculated by the wire tension at the entrance of the Bowden-cable. For the friction loss problem in Bowden-cable transmission, the variation range of the sum of the cumulative angles of the Bowden-cable paths is limited to the range of 0°~ 90° by the physical method. The friction compensation is performed by the method of median compensation. Finally, the grasping force control experiment of the soft exoskeleton glove proves the effectiveness of the static mechanics model of the finger and the friction compensation method. The maximum range of the fingertip force error is within ±1 N. A grasping experiment is performed on the patient without the hand movement function to verify the actual application effect of the soft exoskeleton glove. The experimental results show that the soft exoskeleton glove can assist patients to reliably grasp daily necessities.
soft exoskeleton|bionics|friction compensation|static mechanical model|grasping force control[1] 王陇德.中国脑卒中防治报告(2015)[M].北京:中国协和医科大学出版社, 2015. Wang L D. Chinese stroke prevention report (2015)[M]. Beijing:Peking Union Medical College Press, 2015. [2] Beebe J A, Lang C E. Active range of motion predicts upper extremity function 3 months after stroke[J]. Stroke, 2009, 40(5):1772-1779. [3] Reinkensmeyer D J, Emken J L, Cramer S C. Robotics, motor learning, and neurologic recovery[J]. Annual Review of Biomedical Engineering, 2004, 6:497-525. [4] Takahashi C D, Der-Yeghiaian L, Le V, et al. Robot-based hand motor therapy after stroke[J]. Brain, 2008, 131(2):425-437. [5] Chiri A, Vitiello N, Giovacchini F, et al. Mechatronic design and characterization of the index finger module of a hand exoskeleton for post-stroke rehabilitation[J]. IEEE/ASME Transactions on Mechatronics, 2012, 17(5):884-894. [6] Ma Z, Ben-Tzvi P. RML glove-An exoskeleton glove mechanism with haptics feedback[J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(2):641-652. [7] Lee S W, Landers K A, Park H S. Development of a biomimetic hand exotendon device (BiomHED) for restoration of functional hand movement post-stroke[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(4):886-898. [8] Biggar S, Yao W. Design and evaluation of a soft and wearable robotic glove for hand rehabilitation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(10):1071-1080. [9] In H, Kang B B, Sin M K, et al. Exo-Glove:A wearable robot for the hand with a soft tendon routing system[J]. IEEE Robotics and Automation Magazine, 2015, 22(1):97-105. [10] Xiloyannis M, Cappello L, Binh K D, et al. Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living[J]. Journal of Rehabilitation and Assistive Technologies Engineering, 2017, 4(1). DOI:10.1177/2055668316680315. [11] Popov D, Gaponov I, Ryu J H. Portable exoskeleton glove with soft structure for hand assistance in activities of daily living[J]. IEEE/ASME Transactions on Mechatronics, 2017, 22(2):865-875. [12] Nycz C J, Delph M A, Fischer G S. Modeling and design of a tendon actuated soft robotic exoskeleton for hemiparetic upper limb rehabilitation[C]//37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway, USA:IEEE, 2015:3889-3892. [13] Napier J R. The prehensile movements of the human hand[J]. Journal of Bone and Joint Surgery:British Volume, 1956, 38(4):902-913. [14] 史士财,高晓辉,姜力,等.欠驱动自适应机器人手的研制[J].机器人, 2004, 26(6):496-501. Shi S C, Gao X H, Jiang L, et al. Development of the underactuated self-adaptive robotic hand[J]. Robot, 2004, 26(6):496-501. [15] 王启申,李继婷.手康复机器人钢丝绳-绳套传动系统中的摩擦补偿[J].机器人, 2014, 36(1):1-7. Wang Q S, Li J T. Friction compensation in cable-conduit transmission system of hand rehabilitation robot[J]. Robot, 2014, 36(1):1-7. [16] Jeong U, Cho K J. Feedforward friction compensation of Bowden-cable transmission via loop routing[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, USA:IEEE, 2015:5948-5953. [17] 李佳.套索传动系统建模及柔顺控制研究[D].南京:东南大学, 2016. Li J. Modeling and compliance control of tendon-sheath transmission system[D]. Nanjing:Southeast University, 2016. [18] Smaby N, Johanson M E, Baker B, et al. Identification of key pinch forces required to complete functional tasks[J]. Journal of Rehabilitation Research & Development, 2004, 41(2):215-223.