仿人足底肌电特征的机器人行走规划

孙广彬; 王宏; 陆志国; 王福旺; 史添玮; 王琳

doi:10.16383/j.aas.2015.c140632

仿人足底肌电特征的机器人行走规划

doi: 10.16383/j.aas.2015.c140632

1.
东北大学机械工程与自动化学院沈阳 110819

基金项目:

国家自然科学基金(61071057, 51405073),辽宁省高等学校创新团队项目(LT2014006)资助

详细信息

作者简介:
孙广彬东北大学机械工程与自动化学院博士研究生. 主要研究方向为仿人机器人智能控制.E-mail: sunguangbin2@163.com

通讯作者:
王宏东北大学机械工程与自动化学院教授. 主要研究方向为生物机械电子工程, 人机交互与融合, 生理电信号分析与利用, 机器学习. E-mail: hongwang@mail.neu.edu.cn

计量
- 文章访问数: 1804
- HTML全文浏览量: 95
- PDF下载量: 1539
- 被引次数: 0
出版历程
- 收稿日期: 2014-09-02
- 修回日期: 2015-01-13
- 刊出日期: 2015-05-20

Humanoid Walking Planning Based on EMG from Human Foot-bottom

1.
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819

Funds:

Supported by National Natural Science Foundation of China (61071057, 51405073) and the University Innovation Team of Liaoning Province (LT2014006)

摘要

摘要: 模仿人类行走规律是规划双足机器人运动的基础.以往模仿人类步态主要通过视觉方法或惯性模块测量(Inertia measurement unit, IMU)方法捕捉人体特征点轨迹.这些方法不考虑零力矩点(Zero moment point, ZMP)的相似性.为解决该问题,本文提出了一种基于足底肌电信号(Electromyography, EMG)和惯性模块测量信号的混合运动规划方法.该方法通过测量足底肌电信号计算出足底压力中心的位置以及踝关节扭矩,结合惯性模块所测量的人体躯干和双足轨迹,来规划双足机器人的步态.首先,用肌电仪测量足底肌电信号,用惯性测量模块测量人体各肢体部分的姿态轨迹,经数据标定后作为仿人机器人的运动参考; 然后,通过预观控制输出稳定的步态.为确保仿人行走的效果,基于人体相似性对运动数据进行了步态优化.实验验证和分析表明, EMG信号超前ZMP约160ms,利用这个特性实现了对压力点位置的有效预测,提高了机器人在线模仿人类行走的稳定性.
- 仿人机器人 /
- 拟人行走规划 /
- 预观控制 /
- 足底肌电信号
Abstract: The research on mimicking human-like walk lays a basis for biped walking motion. The conventiaonal method of realising human-like walk is using computer vision or inertia measurement unit (IMU) to capture the feature points of human body. However, these methods do not consider the zero moment point (ZMP) similarity. To tackle this problem, this paper proposes a hybrid motion planning method based on foot-bottom electromyography (EMG) signal and IMU measurement. This method uses the measurement of EMG under foot bottom to estimate the ZMP and ankle torque and plans the robot gait combining the measurement of trajectories of human trunk and feet. First, the bionic data from human is calibrated and transformed as motion reference for the robot. Then the dynamic balanced walking motion is generated by preview control. To ensure the human-like feature, the planned motion is optimised based on human similarity. Finally, it is validated by experiments and analysis that EMG signal is advanced 160ms before ZMP change. The short time prediction of ZMP is realized with higher degree of likeness with human motion.
- Humanoid robot /
- human-like walk planning /
- preview control /
- electromyography (EMG)

HTML全文

参考文献(35)

[1]	Johansson G. Visual perception of biological motion and a model for its analysis. Perception and Psychophysics, 1973, 14(2): 201-211
[2]	[2] Troje NF. Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. Journal of vision, 2002, 2(5): 2
[3]	[3] Yamane K, Nakamura Y. Natural motion animation through constraining and deconstraining at will. IEEE Transactions on Visualization and Computer Graphics, 2003, 9(3): 352-360
[4]	[4] Dasgupta A, Nakamura Y. Making feasible walking motion of humanoid robots from human motion capture data. 1999 Proceedings 1999 IEEE International Conference on Robotics and Automation: IEEE 1999. 1044-1049
[5]	[5] Hofmann A, Popovic M, Herr H. Exploiting angular momentum to enhance bipedal center-of-mass control. IEEE International Conference on Robotics and Automation (ICRA): IEEE 2009. 4423-4429
[6]	[6] Harada K, Miura K, Morisawa M, Kaneko K, Nakaoka S, Kanehiro F, Tsuji T, et al. Toward human-like walking pattern generator. IEEE/RSJ International Conference on Intelligent Robots and Systems 2009. 1071-1077
[7]	[7] Kajita S, Miura K, Morisawa M, Kaneko K, Kanehiro F, Yokoi K. Evaluation of a stabilizer for biped walk with toe support phase. 12th IEEE-RAS International Conference on Humanoid Robots 2012. 586-592
[8]	[8] Galdeano D, Bonnet V, Bennehar M, Fraisse P, Chemori A. Partial human data in design of human-like walking control in humanoid robotics. SYROCO 2012. 10(1): 485-490
[9]	[9] Miura K, Morisawa M, Kanehiro F, Kajita S, Kaneko K, Yokoi K. Human-like walking with toe supporting for humanoids. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): IEEE 2011. 4428-4435
[10]	Ogura Y, Shimomura K, Kondo H, Morishima A, Okubo T, Momoki S, Hun-ok L, Takanishi A. Human-like walking with knee stretched, heel-contact and toe-off motion by a humanoid robot. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems: IEEE 2006. 3976-3981
[11]	Collins SH, Ruina A. A bipedal walking robot with efficient and human-like gait. Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005): IEEE 2005. 1983-1988
[12]	Nakaoka S, Nakazawa A, Yokoi K, Hirukawa H, Ikeuchi K. Generating whole body motions for a biped humanoid robot from captured human dances. 2003 IEEE International Conference on Robotics and Automation (ICRA 2003): IEEE 2003. 3905-3910
[13]	Huang Q, Peng Z, Zhang W, Zhang L, Li K. Design of humanoid complicated dynamic motion based on human motion capture. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005): IEEE 2005. 3536-3541
[14]	Nakaoka S, Nakazawa A, Kanehiro F, Kaneko K, Morisawa M, Ikeuchi K. Task model of lower body motion for a biped humanoid robot to imitate human dances. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005): IEEE 2005. 3157-3162
[15]	Miura K, Morisawa M, Nakaoka S, Kanehiro F, Harada K, Kaneko K, Kajita S. Robot motion remix based on motion capture data towards human-like locomotion of humanoid robots. 9th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2009): IEEE 2009. 596-603
[16]	Ott C, Lee D, Nakamura Y. Motion capture based human motion recognition and imitation by direct marker control. 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2008): IEEE 2008. 399-405
[17]	Suleiman W, Yoshida E, Kanehiro F, Laumond J-P, Monin A. On human motion imitation by humanoid robot. IEEE International Conference on Robotics and Automation (ICRA 2008): IEEE 2008. 2697-2704
[18]	Kim S-K, Hong S, Kim D. A walking motion imitation framework of a humanoid robot by human walking recognition from IMU motion data. 9th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2009): IEEE 2009. 343-348
[19]	Zhao X, Huang Q, Peng Z, Li K. Kinematics mapping and similarity evaluation of humanoid motion based on human motion capture. Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004): IEEE 2004. 840-845
[20]	Zhao X J, Huang Q, Peng Z Q, Zhang L G, Li K J. Kinematics mapping of humanoid motion based on human motion. Robotics, 2005, 27(4): 358-361
[21]	Kajita S, Kanehiro F, Kaneko K, Fujiwara K, Harada K, Yokoi K, Hirukawa H. Biped walking pattern generation by using preview control of zero-moment point. Proceedings of IEEE International Conference on Robotics and Automation 2003. 2: 1620-1626
[22]	Park J, Youm Y. General ZMP preview control for bipedal walking. IEEE International Conference on Robotics and Automation 2007. 2682-2687
[23]	Katayama T, Ohki T, Inoue T, Kato T. Design of an optimal controller for a discrete-time system subject to previewable demand. International Journal of Control, 1985, 41(3): 677-699
[24]	Nishiwaki K, Satoshi K. Simultaneous planning of CoM and ZMP based on the preview control method for online walking control. 11th IEEE-RAS International Conference on Humanoid Robots: IEEE 2011. 745-751
[25]	Nishiwaki K, Kagami S. Strategies for adjusting the ZMP reference trajectory for maintaining balance in humanoid walking. IEEE International Conference on Robotics and Automation: IEEE 2010. 4230-4236
[26]	Kim C, Kim D, Oh Y. Adaptation of human motion capture data to humanoid robots for motion imitation using optimization. Integrated computer-aided engineering, 2006, 13(4): 377-389
[27]	Mombaur K, Truong A, Laumond J-P. From human to humanoid locomotion an inverse optimal control approach. Autonomous robots, 2010, 28(3): 369-383
[28]	Koenemann J, Burget F, Bennewitz M. Real-time Imitation of Human Whole-Body Motions by Humanoids. Autonomous Robots, 2013.
[29]	Do M, Azad P, Asfour T, Dillmann R. Imitation of human motion on a humanoid robot using non-linear optimization. 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2008): IEEE 2008. 545-552
[30]	Kanehiro F, Suleiman W, Miura K, Morisawa M, Yoshida E. Feasible pattern generation method for humanoid robots. 9th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2009): IEEE 2009. 542-548
[31]	de Lasa M, Mordatch I, Hertzmann A. Feature-based locomotion controllers. ACM Transactions on Graphics (TOG), 2010, 29(4): 131
[32]	Mordatch I, De Lasa M, Hertzmann A. Robust physics-based locomotion using low-dimensional planning. ACM Transactions on Graphics (TOG), 2010, 29(4): 71
[33]	Yamane K, Hodgins J. Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009): IEEE 2009. 2510-2517
[34]	Ferreira JP, Crisstomo M, Coimbra AP. ZMP trajectory reference for the sagittal plane control of a biped robot based on a human CoP and gait. 2009 IROS 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems: IEEE 2009. 1588-1593
[35]	Vukobratovic M, Stepanenko J. On the stability of anthropomorphic systems. Mathematical Biosciences, 1972, 15(1): 1-$37