可穿戴助力机器人传感器信号预测算法和控制器的设计
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摘要
可穿戴助力机器人是一种典型的辅助型康复机器人,是一款帮助人们扩展下肢运动能力的外骨骼助力装置,它的基本原理是通过感知系统获得人体的运动意图信息,根据这些信息,控制系统控制安装在髋关节和膝关节处的直流伺服电机来驱动连杆运动,从而达到对人体提供助力的目的。可穿戴助力机器人的感知系统由二维力传感器、一维力传感器和角度传感器组成,其中二维力传感器分别安装在大腿连杆和小腿连杆上;一维力传感器安装在脚底板上;角度传感器直接采用直流伺服电机后面的光电编码器。在实际的操作过程中,感知系统能否及时准确地获取人体的运动信息成为可穿戴助力机器人研究的一个关键。为了提高感知系统中传感器的动态响应频率,以保证整个系统的实时性,我们提出了一种新颖的基于时间序列分析的传感器信号在线预测算法,该算法可在线使用并成倍地提高传感器的动态响应频率。同时为了实现对控制系统的体积小、重量轻、扩展性好等要求,我们设计并制作了第一代直流伺服电机的嵌入式运动控制器,该控制器直接采用了运动控制芯片LM629和H桥驱动芯片LMD18200,实现了高集成度,简化了系统的设计。
本文是在国家自然科学基金“可穿戴型智能助力机器人技术研究”(No.60575054)和国家863项目“可穿戴型助老助残机器人示范平台”(No.2006AA040204)的资助下完成的。
本文的主要内容如下:
1.基于时间序列分析的传感器信号预测算法的设计:针对预测算法的运算量小、精度高、可在线使用等要求,构造了一种新颖的基于时间序列分析的传感器信号在线预测算法。该算法由自回归(AR)模型、递归最小二乘法(RLS)和最小预报误差准则(FPE)组成。
2.预测算法实现的软硬件系统设计:针对预测算法实现系统实时、高速的的要求,设计了相关的软硬件系统。该系统的硬件部分由信号调理电路和信号采集、处理和传输电路组成。系统的软件设计包括上位机部分和下位机部分,其中下位机部分主要是单片机中软件的编写;上位机部分是采用了VC++与MATLAB混合编程来实现预测算法。
3.传感器信号预测算法的仿真和实验:首先使用了MATLAB来计算预测算法的结果,通过预测值与实测值进行比较,验证了该在线预测算法的有效性,同时也推导出预测误差与预测步长之间的关系。最后,我们采用了上面设计的系统来进行实验,实验结果充分验证了该算法的有效性和实用性。
4.可穿戴助力机器人运动控制器设计:针对控制系统的体积小、重量轻、集成度高、扩展性好等要求,我们设计了第一代直流伺服电机运动控制器。该控制器采用了嵌入式系统设计,集成度高,可以通过串口与计算机进行通信和实现调试,为今后的整个嵌入式控制系统设计打下了基础。
The Wearable Power Assist Robot is a kind of assistive rehabilitation robot. It is an autonomous exoskeleton which is to help people to expand the capacity of the lower limbs motion. In order to acquire the intention of human body movement, the sensing system obtains the sensing information from human body, based on this sensing information, and then the control system takes charge of the DC servo motors which are placed on the hip joints and knee joints to provide the assistive power. The sensing system of the Wearable Power Assist Robot is built up with the two-dimensional force sensors, the one-dimensional force sensors and the angle sensors. The goal of using the force sensors is to acquire the intention of human body movements by means of detecting force. The two-dimensional force sensors are placed on the thigh link and the lower thigh link, and the one-dimensional force sensors are placed in the footboard, and the encoders in the DC motors are used as angle sensors. In practice, whether the sensing system can acquire human motion intention from human body accurately and rapidly is becoming the key issue in our research. In order to improve the dynamic response of the exoskeleton and ensure the real-time quality of the whole system, we propose a novel sensing information forecasting algorithm which is based on the Time Series Analysis. This forecasting algorithm can be used on-line and increase the dynamic response of the sensing system many times. In order to achieve the control system of small size, light weight and good scalability, we also design and produce the first generation of motion controller for the DC servo motor which is utilized in the Wearable Power Assist Robot, this motion controller mainly utilizes LM629 which is a precision motion controller chip and LMD18200 which is a H-bridge driver chip for motion control applications. We can simplify the system design and increase the system integration by using them.
This paper is supported by the National Science Foundation of China (Grant #60575054) and Foundation of 863 Program (Grant #2006AA040204).
The main contents of the paper are as follows:
1. The design of the sensing forecasting algorithm based on the time series analysis: In order to meet the requirement of small amount of computation, high precision and can be used on-line, we build up a novel sensing information forecasting algorithm, the algorithm is made up with the autoregressive model, the recursive least square method and the final prediction error order selection criterion.
2. The design of software and hardware system for the sensing information forecasting algorithm: In order to meet the requirement of real-time and high speed, we design the software and hardware system for the sensing information forecasting algorithm. The hardware is made up with the signal conditioning circuit and the signal acquisition, processing and transmission circuit. The software can be categorized into the software in MCU and the software in PC, and the software in PC is achieved by the VC++ and MATLAB mixed programming.
3. The simulations and experiments of the sensing information forecasting algorithm: Firstly, we utilize MATLAB to calculate the results of the algorithm, and then, we can compare the predictive value with the measured value to verify the validity of the algorithm and derive the relationship between the forecasting error and the forecasting steps. Finally, the correlative experiments have been carried out, and the results fully demonstrate the effectiveness and practicality of the sensing information forecasting algorithm.
4. The design of motion controller for the Wearable Power Assist Robot: In order to meet the requirement of small size, light weight, high integration and good expansion, we design the first generation of the motion controller. This controller uses the embedded system to achieve a high level of integration. Through the serial port, the controller can be debugged and achieve communication. The motion controller lays the foundation for the future whole embedded control system design.
本文是在国家自然科学基金“可穿戴型智能助力机器人技术研究”(No.60575054)和国家863项目“可穿戴型助老助残机器人示范平台”(No.2006AA040204)的资助下完成的。
本文的主要内容如下:
1.基于时间序列分析的传感器信号预测算法的设计:针对预测算法的运算量小、精度高、可在线使用等要求,构造了一种新颖的基于时间序列分析的传感器信号在线预测算法。该算法由自回归(AR)模型、递归最小二乘法(RLS)和最小预报误差准则(FPE)组成。
2.预测算法实现的软硬件系统设计:针对预测算法实现系统实时、高速的的要求,设计了相关的软硬件系统。该系统的硬件部分由信号调理电路和信号采集、处理和传输电路组成。系统的软件设计包括上位机部分和下位机部分,其中下位机部分主要是单片机中软件的编写;上位机部分是采用了VC++与MATLAB混合编程来实现预测算法。
3.传感器信号预测算法的仿真和实验:首先使用了MATLAB来计算预测算法的结果,通过预测值与实测值进行比较,验证了该在线预测算法的有效性,同时也推导出预测误差与预测步长之间的关系。最后,我们采用了上面设计的系统来进行实验,实验结果充分验证了该算法的有效性和实用性。
4.可穿戴助力机器人运动控制器设计:针对控制系统的体积小、重量轻、集成度高、扩展性好等要求,我们设计了第一代直流伺服电机运动控制器。该控制器采用了嵌入式系统设计,集成度高,可以通过串口与计算机进行通信和实现调试,为今后的整个嵌入式控制系统设计打下了基础。
The Wearable Power Assist Robot is a kind of assistive rehabilitation robot. It is an autonomous exoskeleton which is to help people to expand the capacity of the lower limbs motion. In order to acquire the intention of human body movement, the sensing system obtains the sensing information from human body, based on this sensing information, and then the control system takes charge of the DC servo motors which are placed on the hip joints and knee joints to provide the assistive power. The sensing system of the Wearable Power Assist Robot is built up with the two-dimensional force sensors, the one-dimensional force sensors and the angle sensors. The goal of using the force sensors is to acquire the intention of human body movements by means of detecting force. The two-dimensional force sensors are placed on the thigh link and the lower thigh link, and the one-dimensional force sensors are placed in the footboard, and the encoders in the DC motors are used as angle sensors. In practice, whether the sensing system can acquire human motion intention from human body accurately and rapidly is becoming the key issue in our research. In order to improve the dynamic response of the exoskeleton and ensure the real-time quality of the whole system, we propose a novel sensing information forecasting algorithm which is based on the Time Series Analysis. This forecasting algorithm can be used on-line and increase the dynamic response of the sensing system many times. In order to achieve the control system of small size, light weight and good scalability, we also design and produce the first generation of motion controller for the DC servo motor which is utilized in the Wearable Power Assist Robot, this motion controller mainly utilizes LM629 which is a precision motion controller chip and LMD18200 which is a H-bridge driver chip for motion control applications. We can simplify the system design and increase the system integration by using them.
This paper is supported by the National Science Foundation of China (Grant #60575054) and Foundation of 863 Program (Grant #2006AA040204).
The main contents of the paper are as follows:
1. The design of the sensing forecasting algorithm based on the time series analysis: In order to meet the requirement of small amount of computation, high precision and can be used on-line, we build up a novel sensing information forecasting algorithm, the algorithm is made up with the autoregressive model, the recursive least square method and the final prediction error order selection criterion.
2. The design of software and hardware system for the sensing information forecasting algorithm: In order to meet the requirement of real-time and high speed, we design the software and hardware system for the sensing information forecasting algorithm. The hardware is made up with the signal conditioning circuit and the signal acquisition, processing and transmission circuit. The software can be categorized into the software in MCU and the software in PC, and the software in PC is achieved by the VC++ and MATLAB mixed programming.
3. The simulations and experiments of the sensing information forecasting algorithm: Firstly, we utilize MATLAB to calculate the results of the algorithm, and then, we can compare the predictive value with the measured value to verify the validity of the algorithm and derive the relationship between the forecasting error and the forecasting steps. Finally, the correlative experiments have been carried out, and the results fully demonstrate the effectiveness and practicality of the sensing information forecasting algorithm.
4. The design of motion controller for the Wearable Power Assist Robot: In order to meet the requirement of small size, light weight, high integration and good expansion, we design the first generation of the motion controller. This controller uses the embedded system to achieve a high level of integration. Through the serial port, the controller can be debugged and achieve communication. The motion controller lays the foundation for the future whole embedded control system design.
引文
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[2]全国老龄工作委员会办公室.中国人口老龄化发展趋势预测研究报告[J].中国妇运, 2007,(02):4.
[3]陈峰.可穿戴型助力机器人技术研究[D].合肥:中国科学技术大学, 2007.
[4] B.J. Makinson, General Electric CO.. Research and Development Prototype for Machine Augmentation of Human Strength and Endurance, Hardiman I Project, General Electric Report S-71-1056 [R], Schenectady, NY, 1971.
[5] Adam Zoss, H.Kazerooni, Andrew Chu. On the Mechanical Design of the Berkeley Lower Extremity Exoskeleton (BLEEX)[C], IEEE/RSJ International Conference on Intelligent Rotbots and Systems, 2005: 3132-3139.
[6] http://www.gizmag.co.uk/go/1604/.
[7] http://www.21stcentury.co.uk/technology/solotrek_xfv.asp.
[8] Berkley Robotics Laboratory.“Berkeley Exoskeleton (BLEEX)”, http:// bleex.me. berkeley.edu/bleex.htm.
[9] K. Amundson, J. Raade, N. Harding, H.Kazerooni. Hybrid Hydraulic-Electric Power Unit for Field and Service Robots[C], IEEE/RSJ International Conference on intelligent Robots and Systems, 2005: 2053-2058.
[10] H.Kazerooni,“Exoskeletons for Human Power Augmentation”. [C], 2005IEEE/RSJ International Conference on intelligent Robots and Systems, Page(s): 3120-3125.
[11] Jerry E.Pratt, Benjamin T.Krupp, Christopher J.Morse, The RoboKnee. An Exoskeleton for Enhancing Strength and Endurance During Walking[C], Proceedings of the 2004 IEEE International Conference on Robotics & Automation, New Orleans, LA, April 2004: 2430-2435.
[12] Yamamoto, Keijiro; Hyodo, Kazuhito; Ishi, Mineo; Matsuo Takashi, T. Development of power assisting suit for assisting nurse labor[J], JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, Sep. 2002, 45(3): 703-711.
[13] Keijiro Yamamoto, Mineo Ishi, Hirokazu Noborisaka, Kazuhito Hyodo, Stand Alone Wearable Power Assisting Suit-Sensing and Control Systems[C], Proceeding of the 2004 IEEE International Workshop on Robot and Human Interactive Communication Kurashiki, Okayama Japan Setptember 20-22, 2004: 661-666.
[14] Kota Kasaoka, Yoshiyuki Sankai. Predictive Control Estimating Operator’s Intention forStepping-up Motion by Exo-Sckeleton Type Power Assist System HAL[C], Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii, USA, Oct.29-Nov.03, 2001: 1578-1583.
[15] S. Lee, Y. Sankai. Power Assist Control for Walking Aid with HAL-3 Based on EMG and Impedance Adjustment around Knee Joint[C], Proceedings of the 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, Lausanne, Switzerland, October 2002: 1499-1504.
[16] Hiroaki Kawamoto, Shigehiro Kanbe. Power Assist Method for HAL-3 Estimating Operator’s Intention Based on Motion Information[C], Proceed125ing of the 2003 IEEE International Workshop on Robots and Human Interactive Communication, Millbrae, California, USA, Oct. 31-Nov. 2, 2003: 67-72.
[17] Hiroaki Kawamoto, Suwoong Lee, Shigehiro Kanbe, Yoshihuki Sankai. Power Assist Method for HAL-3 using EMG-based Feedback Controller[C], IEEE International Conference on System, Man and Cybemetics, 5-8 Oct. 2003: 1648-1653.
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