Abstract: We proposed a lower extremity exoskeleton for power amplification that perceives intended human motion via human-exoskeleton interaction signals measured by biomedical or mechanical sensors, and estimates human gait trajectories to implement corresponding actions quickly and accurately. In this study, torque sensors mounted on the exoskeleton links are proposed for obtaining physical human-robot interaction (pHRI) torque information directly. A kalman smoother is adopted for eliminating noise and smoothing the signal data. Simultaneously, the mapping from the pHRI torque to the human gait trajectory is defined. The mapping is derived from the real-time state of the robotic exoskeleton during movement. The walking phase is identified by the threshold approach using ground reaction force. Based on phase identification, the human gait can be estimated by applying the proposed algorithm, and then the gait is regarded as the reference input for the controller. A proportional-integral-derivative control strategy is constructed to drive the robotic exoskeleton to follow the human gait trajectory. Experiments were performed on a human subject who walked on the floor at a natural speed wearing the robotic exoskeleton. Experimental results show the effectiveness of the proposed strategy.

一种应用于功率放大液压驱动外骨骼的基于物理人机交互估计的控制策略

[1]Aphiratsakun N, Parnichkun M, 2010. Balancing control of leg exoskeleton using ZMP-based Jacobian compensation. Int J Rob Autom, 25(4):359-371.

[2]Chen SY, 2012. Kalman filter for robot vision: a survey. IEEE Trans Ind Electron, 59(11):4409-4420.

[3]Deng XH, Shen HH, Chen F, et al., 2007. Motion information acquisition from human lower limbs for wearable robot. Int Conf on Information Acquisition, p.137-142.

[4]Dollar AM, Herr H, 2008. Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans Rob, 24(1):144-158.

[5]Fleischer C, Hommel G, 2008. A human-exoskeleton interface utilizing electromyography. IEEE Trans Rob, 24(4):872-882.

[6]George T, Shalu GK, Sivanandan KS, 2011. Sensing, processing and application of EMG signals for HAL (hybrid assistive limb). Int Conf on Sustainable Energy and Intelligent Systems, p.749-753.

[7]Kasaoka K, Sankai Y, 2001. Predictive control estimating operator’s intention for stepping-up motion by exo-skeleton type power assist system HAL. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.1578-1583.

[8]Kazerooni H, Racine JL, Huang L, 2005. On the control of the Berkeley lower extremity exoskeleton (BLEEX). IEEE Int Conf on Robotics and Automation, p.4353-4360.

[9]Kiguchi K, Imada Y, 2009. EMG-based control for lower-limb power-assist exoskeletons. IEEE Workshop on Robotic Intelligence in Informationally Structured Space, p.19-24.

[10]Lee H, Kim W, Han J, 2012. The technical trend of the exoskeleton robot system for human power assistance. Int J Prec Eng Manuf, 13(8):1491-1497.

[11]Lee HD, Yu SN, Lee SH, et al., 2008. Development of human-robot interfacing method for assistive wearable robot of the human upper extremities. Int Conf on Instrumentation, Control and Information Technology (SICE Annual Conf), p.1755-1760.

[12]Lobo-Prat J, Keemink AQL, Stienen AHA, et al., 2014. Evaluation of EMG, force and joystick as control interfaces for active arm supports. J Neuroeng Rehabil, 11(1):68.

[13]Long Y, Du ZJ, Wang WD, 2015. A fuzzy logic system tuned with particle swarm optimization for gait segmentation using insole measured ground reaction force. World Congress on Intelligent Control and Automation, p.513-518.

[14]Long Y, Du ZJ, Wang WD, et al., 2016a. Development of a wearable exoskeleton rehabilitation system based on hybrid control mode. Int J Adv Rob Syst, 13(5):1-10.

[15]Long Y, Du ZJ, Wang WD, et al., 2016b. PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons. Sensors, 16(9):1408.

[16]Long Y, Du ZJ, Wang WD, et al., 2016c. Robust sliding mode control based on GA optimization and CMAC compensation for lower limb exoskeleton. Appl Bion Biomech, 2016:5017381.

[17]Long Y, Du ZJ, Dong W, et al., 2017. Human gait trajectory learning using online Gaussian process for assistive lower limb exoskeleton. In: Yang CJ, Virk GS, Yang HY (Eds.), Wearable Sensors and Robots. Springer, Singapore, p.165-179.

[18]Mishra AK, Srivastava A, Tewari RP, et al., 2012. EMG analysis of lower limb muscles for developing robotic exoskeleton orthotic device. Proc Eng, 41:32-36.

[19]Najarian K, Splinter R, 2012. Biomedical Signal and Image Processing. CRC Press, Boca Raton, USA.

[20]Pons JL, 2008. Wearable Robots: Biomechatronic Exoskeletons. John Wiley & Sons, Hoboken, USA, p.127-163.

[21]Sankai Y, 2011. HAL: hybrid assistive limb based on cybernics. In: Kaneko M, Nakamura Y (Eds.), Robotics Research. Springer Berlin Heidelberg, p.25-34.

[22]Sarkka S, Vehtari A, Lampinen J, 2004. Time series prediction by Kalman smoother with cross-validated noise density. IEEE Int Joint Conf on Neural Networks, p.1653-1657.

[23]Welch G, Bishop G, 2001. An Introduction to the Kalman Filter. Technical Report, University of North Carolina, Chapel Hill, USA, p.1-16.

[24]Yamamoto K, Ishii M, Noborisaka H, et al., 2004. Standalone wearable power assisting suit-sensing and control systems. IEEE Int Workshop on Robot and Human Interactive Communication, p.661-666.

[25]Yin YH, Fan YJ, Xu LD, 2012. EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton. IEEE Trans Inform Technol Biomed, 16(4):542-549.

[26]Yoshimitsu T, Yamamoto K, 2004. Development of a power assist suit for nursing work. SICE Annual Conf, p.577-580.

[27]Zoss AB, Kazerooni H, Chu A, 2006. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Trans Mechatron, 11(2):128-138.