兼具柔顺与安全的助行机器人运动控制研究

徐文霞; 黄剑; 晏箐阳; 王永骥; 陶春静

doi:10.16383/j.aas.2016.c160163

兼具柔顺与安全的助行机器人运动控制研究

doi: 10.16383/j.aas.2016.c160163

徐文霞^1,,
黄剑^1, ,,
晏箐阳^1,,
王永骥^1,,
陶春静^2,

1.
华中科技大学自动化学院图像处理与智能控制教育部重点实验室武汉 430074
2.
国家康复辅具研究中心北京 100176

基金项目:

教育部新世纪优秀人才支持计划 NCET-12-0214

国家自然科学基金 61473130

湖北省自然科学基金杰出青年基金 2015CFA047

详细信息

作者简介:
徐文霞华中科技大学自动化学院博士研究生.2008年获得华中科技大学文华学院电气工程及其自动化专业学士学位.主要研究方向为助行机器人运动控制.E-mail:xwxsai@163.com

晏箐阳华中科技大学自动化学院博士研究生.2015年获得华中科技大学测控技术与仪器专业学士学位.主要研究方向为助行机器人运动控制.E-mail:yanqingyang@hust.edu.cn

王永骥华中科技大学自动化学院教授.主要研究方向为飞行器制导控制, 智能优化, 智能控制和康复机器人控制.E-mail:wangyjch@hust.edu.cn

陶春静国家康复辅助研究中心助理研究员.主要研究方向为康复工程, 智能控制和机器人.E-mail:taochj@gmail.com

通讯作者:
黄剑华中科技大学自动化学院教授.2005年获得华中科技大学控制科学与工程博士学位, 曾于2006至2008年在日本名古屋大学微纳米系统工程福田研究室做博士后研究员.主要研究方向为康复机器人, 机器人装配, 网络控制系统和生物信息处理.本文通信作者.E-mail:huang jan@mail.hust.edu.cn

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出版历程
- 收稿日期: 2016-02-25
- 录用日期: 2016-08-15
- 刊出日期: 2016-12-01

Research on Walking-aid Robot Motion Control with Both Compliance and Safety

1.
Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074
2.
National Rehabilitation Center, Beijing 100176

Funds:

Program for New Century Excellent Talents in University NCET-12-0214

National Natural Science Foundation of China 61473130

in part by the Science Fund for Distinguished Young Scholars of Hubei Province 2015CFA047

More Information

Author Bio:
Ph. D. candidate at the School of Automation, Huazhong University of Science and Technology (HUST). She received her bachelor degree from Wenhua College of HUST in 2008. Her main research interest is motion control of walking aid robot

Ph. D. candidate at the School of Automation, Huazhong University of Science and Technology. She received her bachelor degree in measurement techniques and instrumentation from Huazhong University of Science and Technology in 2015. Her main research interest is motion control of walking aid robot

Professor at the School of Automation, Huazhong University of Science and Technology. His research interest covers aircraft guidance and control, intelligent optimization, intelligent control, and rehabilitation robot control

Assistant research fellow at the National Research Centre for Rehabilitation Technical Aids. Her research interest covers rehabilitation engineering, intelligent control, and robotics

Corresponding author: HUANG Jian Professor at the School of Automation, Huazhong University of Science and Technology. He received his Ph. D. degree from Huazhong University of Science and Technology in 2005. From 2006 to 2008, he was a postdoctoral researcher in the Department of MicroNano System Engineering and Department of MechanoInformatics and Systems, Nagoya University, Japan. His research interest covers rehabilitation robot, robotic assembly, networked control systems, and bioinformatics. Corresponding author of this paper

摘要

摘要: 针对助行机器人的柔顺性和安全性问题，基于多传感器系统融合技术，本文提出了一种能够兼具柔顺与安全的助行机器人运动控制方法.首先介绍了助行机器人的机械结构、控制原理以及多传感器系统，然后根据机器人多传感器系统，设计出各传感器相对应的用户意图估计方法，提出了一种基于多传感器融合的助行机器人柔顺运动控制算法.分析用户可能发生的跌倒模式，使用基于卡尔曼滤波（Kalman filter，KF）的序贯概率比检验（Sequential probability ratio test，SPRT）方法和决策函数来判断用户是否会跌倒，并判断处于哪种跌倒模式.最后，通过助行机器人柔顺运动控制实验和用户跌倒检测实验验证了算法的有效性.
- 助行机器人 /
- 跌倒检测 /
- 力传感器 /
- 激光传感器 /
- 柔顺性
Abstract: Aimed at compliance and safety problems in motion control of walking-aid robot, a multi-sensor fusion based walking-aid robot motion control method with both compliance and safety is proposed. Firstly, the mechanism, control theory and multi-sensor system of the walking-aid robot are introduced. Then according to multi-sensor system, user motion intention estimation methods for each sensor are designed and a multi-sensor based compliance motion control for walking-aid robot is proposed. After analyzing user's possible falling modes, a Kalman filter (KF) based sequential probability ratio test (SPRT) method and decision function are used to detect the fall and falling mode. Finally, several compliance motion control experiments and fall detection experiments are described to show validity of the proposed algorithm.
- Walking-aid robot /
- fall detection /
- force sensor /
- laser range finder /
- compliance
注释:

1) 本文责任编委程龙

HTML全文

本文责任编委程龙

图 1 助行机器人

Fig. 1 The intelligent walking-aid robot

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图 2 机器人坐标系系统和交互力的测量

Fig. 2 Robot coordinate system (top view) and measurement of interaction forces

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图 3 圆的参数

Fig. 3 Parameters of circle

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图 4 基于激光的人腿检测

Fig. 4 Leg identification by laser

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图 5 三种跌倒情况

Fig. 5 The three kinds of falling

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图 6 离线跌倒数据采集实验图

Fig. 6 The picture of falling offline data collection experiment

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图 7 3个志愿者在向前跌倒时的意图速度分布图

Fig. 7 Distribution of intent velocities of three subjects during falling to forward

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图 8 3个志愿者在向左跌倒时的意图速度分布图

Fig. 8 Distribution of intent velocities of three subjects during falling to left

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图 9 3个志愿者在向右跌倒时的意图速度分布图

Fig. 9 Distribution of intent velocities of three subjects during falling to right

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图 10 兼具柔顺与安全的助行机器人运动控制系统框图

Fig. 10 The system block diagram of the walking-aid robot motion control which has compliance and safety

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图 11 基于多传感器融合的用户意图速度实验1

Fig. 11 Multi-sensors based human intent velocities Experiment 1

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图 12 基于多传感器融合的用户意图速度实验2

Fig. 12 Multi-sensors based human intent velocities Experiment 2

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图 13 导纳控制实验

Fig. 13 Admittance control experiment

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图 14 交互力对比

Fig. 14 The comparison of interactive force

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图 15 志愿者1向前跌倒仿真实验结果图

Fig. 15 Subject 1 fall detection experiment results of falling to forward

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图 16 志愿者1向左跌倒仿真实验结果图

Fig. 16 Subject 1 fall detection experiment results of falling to left

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图 17 志愿者1向右跌倒仿真实验结果图

Fig. 17 Subject 1 fall detection experiment results of falling to right

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图 18 志愿者2实验结果图

Fig. 18 The fall detection experiment results of Subject 2

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图 19 志愿者3实验结果图

Fig. 19 The fall detection experiment results of Subject 3

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图 20 志愿者4实验结果图

Fig. 20 The fall detection experiment results of Subject 4

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图 21 基于穿戴式传感器的助行机器人用户跌倒检测实验(向前跌倒)

Fig. 21 Wearable sensor based user fall detection experiment of walking-aid robot (fall forward)

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图 22 基于穿戴式传感器的助行机器人用户跌倒检测实验(向左跌倒)

Fig. 22 Wearable sensor based user fall detection experiment of walking-aid robot (fall to left)

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表 1 3个志愿者在不同跌倒模式下的平均意图速度(cm/s)

Table 1 The intent velocities of three subjects in different falling modes (cm/s)

		$\overline{{}{^h}{\dot{X}}_{H}}$	$\overline{{}{^h}{\dot{Y}}_{H}}$	$\overline{{}{^h}{\dot{Z}}_{H}}$	$\overline{{}{^h}{\dot{X}}_{L}}$	$\overline{{}{^h}{\dot{Y}}_{L}}$
A	向前	16	-0.031	0	-11.22	-2.40
	向左	0	16	0	0.27	-7.82
	向右	0	-16	0	-3.97	6.10
B	向前	16	0.051	0	-11.87	-2.09
	向左	0.02	16	0	0.60	-5.83
	向右	0	-16	0	-2.02	6.70
C	向前	16	-0.025	0	-13.95	-4.14
	向左	0	16	0	-1.27	-8.32
	向右	0	-16	0	-2.65	6.13

下载: 导出CSV

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