水下机器人软件可靠性及故障诊断方法研究
摘要
智能水下机器人(AUV)由于工作环境的复杂性、不可预测性,一旦出现故障,轻则造成机器人失效无法完成水下作业任务,重则导致机器人沉没丢失,造成严重损失,因此如何提高其软件可靠性及故障诊断能力已成为AUV研究领域的重要课题。
本文以某智能水下机器人为背景,利用可靠性分配与分析方法,对AUV智能规划与决策控制系统软件各模块进行了可靠性分配及故障模式分析研究,然后针对AUV主要的软件故障模式,建立了基于FMEA的贝叶斯诊断网络。
本文首先分析了水下机器人的软件体系结构,在此基础上,给出了基于重要度、执行情况和复杂度的可靠性分配方法对水下机器人软件模块进行可靠性分配。其中在计算调用系数和复杂度系数时,根据软件实际运行情况,给出了结合Thayer和MEMBOW两种复杂性系数计算方法来权衡复杂度系数,并进行了详细的实现过程。
在得出水下机器人各软件模块的可靠度后,结合FMEA分析了水下机器人主要软件模块的失效模式及影响。然后针对传统FMEA中RPN计算存在的不足,将模糊集理论与灰色关联理论结合对FMEA进行了改进。
最后,针对常见软件故障模式,本文建立了基于FMEA的贝叶斯诊断网络。该网络模型由故障原因,故障模式以及故障影响三层结构组成,并且对单一故障和多故障进行了实验。
实验表明,采用上述方法能有效地提高水下机器人系统的可靠性以及故障诊断能力,确保水下机器人在复杂海洋环境下安全可靠地航行与作业,为进一步的容错控制提供依据,在水下机器人技术中有着重要的现实意义。
In view of the complexity and unpredictability of working conditions, Autonomous Underwater Vehicle (AUV) once takes place of breakdown or runs into trouble, it will result in great losses, such as underwater mission failure or submerge damage which causes serious consequences. Therefore it is very important for AUV to ensure its software reliability and failure diagnosis ability in the AUV research area.
As for software modules of AUV intelligent planning and decision-making control system, a method using software reliability of distribution and analysis technology is researched. Then in the base of underwater vehicle's main failure mode, a Bayesian Network based on FMEA is proposed which is applied into fault diagnosis of AUV.
Firstly, the software architecture of AUV is discussed, and then one reliability allocation method based on importance, operational practice and complexity of AUV software modules is described. And during computing transfer coefficient and complexity coefficient, a new method based on Thayer and MEMBOW is applied to solve the problem of complexity coefficient computation for AUV software modules.
Secondly, after obtaining the reliability of AUV software modules, main software failure modes and effects are analyzed by using FMEA method. Allowing for the insufficiency of traditional FMEA on computing RPN, a FMEA method is improved according to the fuzzy set theory and the gray relational theory.
Finally, taking the common software failure mode into account, a three-layered topological Bayesian diagnostic Network based on FMEA is proposed. This network model consists of failure mode, cause and effect. Meanwhile considering single failure and complex failure, we carry on experiments separately according to Bayesian net.
As a result, experiments show that the above methods can effectively improve the reliability of the AUV system, as well as fault diagnostic capabilities. Furthermore they are ensured that AUV can be capable of navigating and completing underwater task safely and reliably in a complex marine environment. The results also provide the basis for fault-tolerant control which has great practical significance in AUV technology.
本文以某智能水下机器人为背景,利用可靠性分配与分析方法,对AUV智能规划与决策控制系统软件各模块进行了可靠性分配及故障模式分析研究,然后针对AUV主要的软件故障模式,建立了基于FMEA的贝叶斯诊断网络。
本文首先分析了水下机器人的软件体系结构,在此基础上,给出了基于重要度、执行情况和复杂度的可靠性分配方法对水下机器人软件模块进行可靠性分配。其中在计算调用系数和复杂度系数时,根据软件实际运行情况,给出了结合Thayer和MEMBOW两种复杂性系数计算方法来权衡复杂度系数,并进行了详细的实现过程。
在得出水下机器人各软件模块的可靠度后,结合FMEA分析了水下机器人主要软件模块的失效模式及影响。然后针对传统FMEA中RPN计算存在的不足,将模糊集理论与灰色关联理论结合对FMEA进行了改进。
最后,针对常见软件故障模式,本文建立了基于FMEA的贝叶斯诊断网络。该网络模型由故障原因,故障模式以及故障影响三层结构组成,并且对单一故障和多故障进行了实验。
实验表明,采用上述方法能有效地提高水下机器人系统的可靠性以及故障诊断能力,确保水下机器人在复杂海洋环境下安全可靠地航行与作业,为进一步的容错控制提供依据,在水下机器人技术中有着重要的现实意义。
In view of the complexity and unpredictability of working conditions, Autonomous Underwater Vehicle (AUV) once takes place of breakdown or runs into trouble, it will result in great losses, such as underwater mission failure or submerge damage which causes serious consequences. Therefore it is very important for AUV to ensure its software reliability and failure diagnosis ability in the AUV research area.
As for software modules of AUV intelligent planning and decision-making control system, a method using software reliability of distribution and analysis technology is researched. Then in the base of underwater vehicle's main failure mode, a Bayesian Network based on FMEA is proposed which is applied into fault diagnosis of AUV.
Firstly, the software architecture of AUV is discussed, and then one reliability allocation method based on importance, operational practice and complexity of AUV software modules is described. And during computing transfer coefficient and complexity coefficient, a new method based on Thayer and MEMBOW is applied to solve the problem of complexity coefficient computation for AUV software modules.
Secondly, after obtaining the reliability of AUV software modules, main software failure modes and effects are analyzed by using FMEA method. Allowing for the insufficiency of traditional FMEA on computing RPN, a FMEA method is improved according to the fuzzy set theory and the gray relational theory.
Finally, taking the common software failure mode into account, a three-layered topological Bayesian diagnostic Network based on FMEA is proposed. This network model consists of failure mode, cause and effect. Meanwhile considering single failure and complex failure, we carry on experiments separately according to Bayesian net.
As a result, experiments show that the above methods can effectively improve the reliability of the AUV system, as well as fault diagnostic capabilities. Furthermore they are ensured that AUV can be capable of navigating and completing underwater task safely and reliably in a complex marine environment. The results also provide the basis for fault-tolerant control which has great practical significance in AUV technology.
引文
[1]Tarun K.Podder,Mark Sibenac,Hans Thomas,William J.Kirkwood,James G.Bellingham.Reliability growth of autonomous underwater vehicle-Dorado[C].Ocean'04-MTS/IEEETechno-Ocean'04:Bridgesacross the OceansConference Proceedings,2004,2:856-862P
[2]黄锡滋.软件可靠性、安全性与质量保证[M].北京:电子工业出版社,2002.10:191-207页
[3]Christopher Tubb,Geoff Roberts,Edin Omerdic.Development of a fault handling system for remotely operated vehicles[J].IFAC Mechatronic system,Califormia,USA,2002:383-388P
[4]M.Takai,T.Ura.Development of a system to diagnose autonomous underwatervehicle[J].International Journal of Systems Science,1999,30(9):981-988P
[5]Tarun Kanti Podder,Nilanjan Sarkar.Fault Tolerant Decomposition of Thruster Force of an Autonomous Underwater Vehicle[J].Proceedings of the 1999 IEEE International Conference on Robotics & Automation.Detroit,Michigan,1999.5:84-89P
[6]Trun Kanti Podder,Nilanjan Sarkar.Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy[J].Robotics and Autonomous System.2001,34(1):39-52P
[7]J.S.Rae,Stan E.Dunn.On-line Damage Detection for Autonomous Underwater Vehicle[J].IEEE,1994:383-392P
[8]A.Alessandri.Fault diagnosis for nonlinear systems using a bank of neural estimators[J].Computers in Industry,2003(52):271-289P
[9]WANG Lirong.Research on fault diagnosis for AUV control system[D].Harbin:Harbin Engineering University,2006
[10]Wang Yu-jia,Zhang Ming-jun.Study of fuzzy neural networks model for system condition monitoring of AUV[J].Journal of marine science and application,2002,1(2):42-45P
[11]孙瑞琛,自主式水下机器人传感器状态监测技术研究[D].哈尔滨工程大学硕士学位论文.2005
[12]M.B.Daya,A.Raouf.A revised failure mode and effects analysis model[J].International Journal of Quality & Reliability Management,1996,13(1):43P
[13]Garill A.Coles,Jonathan.Young,Use of Failure Modes Effects and Criticality Analysis to Improve Patient Safety[J].American Society of Mechanical Engineers,Safety Engineering and Risk Analysis Division,SERA,2002,12:113-119P
[14]Chandra S.Putcha,Prince Kalia,Frank Pizzano,Gordon Hoskins,Coy Newton,Kunal J.Kamdar.A case study on FMEA applications to system reliability studies[J].International Journal of Reliability,Quality and Safety Engineering,2008,15(2),159-166P
[15]Z.L.Yang,Steve Bonsall,Jin Wang.Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA[J].IEEE Transactions on Reliability,2008,157(3):517-528
[16]黄解军,万幼川,潘和平.贝叶斯网络结构学习及其应用研究[J].武汉大学学报.2004,29(4):315-318页
[17]Albus J,Mccain H,Lumia R.NASA/NBS standard reference model for telerobot control system architecture(NASREM)[R].National Bureau of Standard,NBS Technical Note 1235,1987
[18]Brooks R A.A Robust Layered Control System for a Mobile Robot[J].IEEE J Robotics and Automation,1986,2(1):14-23P
[19]John Loch etc.Software Development for the Autonomous Submersibles Program at MIT Sea Grant and Draper Laboratory[C].In:6~(th) Int Symposium on Untethered Unmanned Submersible,Maryland USA,1989:25-31P
[20]张汝波,顾国昌,杨歌,郭轶尊.具有学习能力的智能机器人体系结构研究[J].华中科技大学学报.2004,S(1):59-60页
[21] Elegbede, A.O.C. Chengbin Chu; Adjallah, K.H., Yalaoui, F. Reliability allocation through cost minimization [J]. IEEE Transactions on Reliability,Issue 1,2003,52(1): 106-111P
[22] D.S. Roy, D.K. Mohanta, A.K. Panda. Software reliability allocation of digital relay for transmission line protection using a combined system hierarchy and fault tree approach [J]. IET Software,2008, 2(5): 437-445P
[23] Chin-Ching chiu, Yi-shiung Yeh. A fast algorithm for reliability-oriented task assignment in a distributed system [J]. Computer communication. 2002,(25): 1622-1630P
[24] Saravana Kumar, Ravindra Babu Misra. Software operational profile based test case allocation using fuzzy logic K [J]. International Journal of Automation and Computing, 2007,4(4): 388-395P
[25] Michael R. Lyu, Sampath Rangarajan, Van Moorsel, Aad P. A..Optimal allocation of test resources for software reliability growth modeling in software development [J]. IEEE Transactions on Reliability, 2002, 51(2):183-192P
[26] Weyuker EJ. Evaluating Software Complexity Measure[J]. IEEE Trans.Software Eng,1988,14(12)
[27] Misra, Sanjay, Akman, Ibrahim. Comparative study of cognitive complexity measures [J]. ISCIS Computer and Information Sciences, 2008
[28] Norman F. Schneidewind. A new way to predict software reliability with parameter evaluation: Shuttle applications [C]. Proceedings-International Conference on Software Engineering,31st Annual IEEE Software Engineering Workshop, SEW-31 2007-Proceedings, 2007, 167-178P
[29] John B.Bowls, Chi Wan.Software Failure Modes and Effects Analysis For a Small Embedded Control System[J].IEEE Proceedings Annual Reliability and Maintain-ability Symposium,2001
[30] Y.M. Wang, K.S. Chin, Gary K. K. Poon, J.B. Yang. Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean [J].Expert Systems with Applications, 2009, 36(2): 1195-1207P
[31] Zhi Chen, K.M. Feng, G.S. Zhang, T. Yuan, C.H. Pan. Preliminary safety research for CH HCSB TBM based on FMEA method [J]. Fusion Engineering and Design, 2008, 83(5-6):743-746P
[32] A. Pillay, J. Wang, G. M. Jung, Y.S. Kwon, C.G. Loughran. Modified FMEA for fishing vessels: A fuzzy set and grey theory approach [C]. Proceedings of the International Offshore and Polar Engineering Conference, 2001, 4:522-528P
[33] John B. Bowles, C. Enrique Pelaez. Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis [J]. Reliability Engineering & System Safety, 1995, 50(2) :203-213P
[34] K. Xu, L.C. Tang, M. Xie, S.L. Ho, M.L. Zhu. Fuzzy assessment of FMEA for engine systems[J].Reliability Engineering and System Safety, 2002,75(1): 17-29P
[35] Antonio C F Guimaraes, Celso Marcelo Franklin Lapa. Effects analysis fuzzy inference system in nuclear problems using approximate reasoning[J]. Annals of Nuclear Energy .2004,31(1): 107-115P
[36] Anand Pillay, Jin Wang. Modified failure mode and effects analysis using approximate reasoning[J].Reliability Engineering and System Safety, 2003,79(1):69-85P
[37] Kai Meng. Tay, C. P. Lim. On the use of fuzzy inference techniques in assessment models: Part II: Industrial applications [J]. Fuzzy Optimization and Decision Making, 2008,7(3): 283-302P
[38] A. C. F. Guimaraes, C. M. F. Lapa. Fuzzy inference to risk assessment on nuclear engineering systems [J]. Applied Soft Computing Journal, 2007,7(1):17-28P
[39] Ching-Liang Chang, Ping-Hung Liu, Chiu-Chi Wei. Failure mode and effects analysis using grey theory [J]. Integrated Manufacturing Systems,2001, 12(3):211-216P
[40] Shih KC, Wu KW, Huang UP. Information of grey relation. Taiwan:Chuan-Hua Co. Ltd, 1996.
[41]Jie Cao,Guo Cao,Wei-wei Wang.A hybrid MCMD integrated borda function and gray rational analysis for 3PLs selection[C].IEEE International Conference on Grey Systems and Intelligent Services,2007:215-220P
[42]肖秦琨,高嵩,高晓光.动态贝叶斯网络推理学习理论及应用[M].北京:国防工业出版社,2007.10
[43]Eunchang.Lee,Yongtae.Park,J.(3.Shin.Large engineering project risk management using a Bayesian belief network[J].Expert Systems with Applications,2009,36(3):5880-5887P
[44]N.Ranganathan,Minesh I.Patel,R.Sathyamurthy.An intelligent system for failure detection and control in an autonomous underwater vehicle[J].IEEE transactions on systems,man,and cybernetics-part A:systems and humans,2001,31(6):762-767P
[45]Francisco J.Torres,Manfred Huber.Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network.Transportation Research Record,2003,(1836):29-36P
[46]Y.Hu,J.H.Chen,J.X.Huang,Liu Mei,Xie Kang.Analyzing software system quality risk using Bayesian belief network[C].Proceedings-2007IEEE International Conference on Granular Computing,2007:93-96P
[47]S.Tesfamariam,B.Martin-Perez.Bayesian belief network to assess carbonation-induced corrosion in reinforced concrete[J].Journal of Materials in Civil Engineering,2008,20(11):707-717P
[48]葛哲学.精通Matlab[M].北京:电子工业出版社,2008.2
[49]Duane Hanselman,Bruce Littlefield.精通Matlab7[M].北京:清华大学出版社,2006
[50]FONG T.A survey of socially interactive robots[J].Robotics and Autonomous Systems,2003,42(3-4):143-16P
[2]黄锡滋.软件可靠性、安全性与质量保证[M].北京:电子工业出版社,2002.10:191-207页
[3]Christopher Tubb,Geoff Roberts,Edin Omerdic.Development of a fault handling system for remotely operated vehicles[J].IFAC Mechatronic system,Califormia,USA,2002:383-388P
[4]M.Takai,T.Ura.Development of a system to diagnose autonomous underwatervehicle[J].International Journal of Systems Science,1999,30(9):981-988P
[5]Tarun Kanti Podder,Nilanjan Sarkar.Fault Tolerant Decomposition of Thruster Force of an Autonomous Underwater Vehicle[J].Proceedings of the 1999 IEEE International Conference on Robotics & Automation.Detroit,Michigan,1999.5:84-89P
[6]Trun Kanti Podder,Nilanjan Sarkar.Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy[J].Robotics and Autonomous System.2001,34(1):39-52P
[7]J.S.Rae,Stan E.Dunn.On-line Damage Detection for Autonomous Underwater Vehicle[J].IEEE,1994:383-392P
[8]A.Alessandri.Fault diagnosis for nonlinear systems using a bank of neural estimators[J].Computers in Industry,2003(52):271-289P
[9]WANG Lirong.Research on fault diagnosis for AUV control system[D].Harbin:Harbin Engineering University,2006
[10]Wang Yu-jia,Zhang Ming-jun.Study of fuzzy neural networks model for system condition monitoring of AUV[J].Journal of marine science and application,2002,1(2):42-45P
[11]孙瑞琛,自主式水下机器人传感器状态监测技术研究[D].哈尔滨工程大学硕士学位论文.2005
[12]M.B.Daya,A.Raouf.A revised failure mode and effects analysis model[J].International Journal of Quality & Reliability Management,1996,13(1):43P
[13]Garill A.Coles,Jonathan.Young,Use of Failure Modes Effects and Criticality Analysis to Improve Patient Safety[J].American Society of Mechanical Engineers,Safety Engineering and Risk Analysis Division,SERA,2002,12:113-119P
[14]Chandra S.Putcha,Prince Kalia,Frank Pizzano,Gordon Hoskins,Coy Newton,Kunal J.Kamdar.A case study on FMEA applications to system reliability studies[J].International Journal of Reliability,Quality and Safety Engineering,2008,15(2),159-166P
[15]Z.L.Yang,Steve Bonsall,Jin Wang.Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA[J].IEEE Transactions on Reliability,2008,157(3):517-528
[16]黄解军,万幼川,潘和平.贝叶斯网络结构学习及其应用研究[J].武汉大学学报.2004,29(4):315-318页
[17]Albus J,Mccain H,Lumia R.NASA/NBS standard reference model for telerobot control system architecture(NASREM)[R].National Bureau of Standard,NBS Technical Note 1235,1987
[18]Brooks R A.A Robust Layered Control System for a Mobile Robot[J].IEEE J Robotics and Automation,1986,2(1):14-23P
[19]John Loch etc.Software Development for the Autonomous Submersibles Program at MIT Sea Grant and Draper Laboratory[C].In:6~(th) Int Symposium on Untethered Unmanned Submersible,Maryland USA,1989:25-31P
[20]张汝波,顾国昌,杨歌,郭轶尊.具有学习能力的智能机器人体系结构研究[J].华中科技大学学报.2004,S(1):59-60页
[21] Elegbede, A.O.C. Chengbin Chu; Adjallah, K.H., Yalaoui, F. Reliability allocation through cost minimization [J]. IEEE Transactions on Reliability,Issue 1,2003,52(1): 106-111P
[22] D.S. Roy, D.K. Mohanta, A.K. Panda. Software reliability allocation of digital relay for transmission line protection using a combined system hierarchy and fault tree approach [J]. IET Software,2008, 2(5): 437-445P
[23] Chin-Ching chiu, Yi-shiung Yeh. A fast algorithm for reliability-oriented task assignment in a distributed system [J]. Computer communication. 2002,(25): 1622-1630P
[24] Saravana Kumar, Ravindra Babu Misra. Software operational profile based test case allocation using fuzzy logic K [J]. International Journal of Automation and Computing, 2007,4(4): 388-395P
[25] Michael R. Lyu, Sampath Rangarajan, Van Moorsel, Aad P. A..Optimal allocation of test resources for software reliability growth modeling in software development [J]. IEEE Transactions on Reliability, 2002, 51(2):183-192P
[26] Weyuker EJ. Evaluating Software Complexity Measure[J]. IEEE Trans.Software Eng,1988,14(12)
[27] Misra, Sanjay, Akman, Ibrahim. Comparative study of cognitive complexity measures [J]. ISCIS Computer and Information Sciences, 2008
[28] Norman F. Schneidewind. A new way to predict software reliability with parameter evaluation: Shuttle applications [C]. Proceedings-International Conference on Software Engineering,31st Annual IEEE Software Engineering Workshop, SEW-31 2007-Proceedings, 2007, 167-178P
[29] John B.Bowls, Chi Wan.Software Failure Modes and Effects Analysis For a Small Embedded Control System[J].IEEE Proceedings Annual Reliability and Maintain-ability Symposium,2001
[30] Y.M. Wang, K.S. Chin, Gary K. K. Poon, J.B. Yang. Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean [J].Expert Systems with Applications, 2009, 36(2): 1195-1207P
[31] Zhi Chen, K.M. Feng, G.S. Zhang, T. Yuan, C.H. Pan. Preliminary safety research for CH HCSB TBM based on FMEA method [J]. Fusion Engineering and Design, 2008, 83(5-6):743-746P
[32] A. Pillay, J. Wang, G. M. Jung, Y.S. Kwon, C.G. Loughran. Modified FMEA for fishing vessels: A fuzzy set and grey theory approach [C]. Proceedings of the International Offshore and Polar Engineering Conference, 2001, 4:522-528P
[33] John B. Bowles, C. Enrique Pelaez. Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis [J]. Reliability Engineering & System Safety, 1995, 50(2) :203-213P
[34] K. Xu, L.C. Tang, M. Xie, S.L. Ho, M.L. Zhu. Fuzzy assessment of FMEA for engine systems[J].Reliability Engineering and System Safety, 2002,75(1): 17-29P
[35] Antonio C F Guimaraes, Celso Marcelo Franklin Lapa. Effects analysis fuzzy inference system in nuclear problems using approximate reasoning[J]. Annals of Nuclear Energy .2004,31(1): 107-115P
[36] Anand Pillay, Jin Wang. Modified failure mode and effects analysis using approximate reasoning[J].Reliability Engineering and System Safety, 2003,79(1):69-85P
[37] Kai Meng. Tay, C. P. Lim. On the use of fuzzy inference techniques in assessment models: Part II: Industrial applications [J]. Fuzzy Optimization and Decision Making, 2008,7(3): 283-302P
[38] A. C. F. Guimaraes, C. M. F. Lapa. Fuzzy inference to risk assessment on nuclear engineering systems [J]. Applied Soft Computing Journal, 2007,7(1):17-28P
[39] Ching-Liang Chang, Ping-Hung Liu, Chiu-Chi Wei. Failure mode and effects analysis using grey theory [J]. Integrated Manufacturing Systems,2001, 12(3):211-216P
[40] Shih KC, Wu KW, Huang UP. Information of grey relation. Taiwan:Chuan-Hua Co. Ltd, 1996.
[41]Jie Cao,Guo Cao,Wei-wei Wang.A hybrid MCMD integrated borda function and gray rational analysis for 3PLs selection[C].IEEE International Conference on Grey Systems and Intelligent Services,2007:215-220P
[42]肖秦琨,高嵩,高晓光.动态贝叶斯网络推理学习理论及应用[M].北京:国防工业出版社,2007.10
[43]Eunchang.Lee,Yongtae.Park,J.(3.Shin.Large engineering project risk management using a Bayesian belief network[J].Expert Systems with Applications,2009,36(3):5880-5887P
[44]N.Ranganathan,Minesh I.Patel,R.Sathyamurthy.An intelligent system for failure detection and control in an autonomous underwater vehicle[J].IEEE transactions on systems,man,and cybernetics-part A:systems and humans,2001,31(6):762-767P
[45]Francisco J.Torres,Manfred Huber.Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network.Transportation Research Record,2003,(1836):29-36P
[46]Y.Hu,J.H.Chen,J.X.Huang,Liu Mei,Xie Kang.Analyzing software system quality risk using Bayesian belief network[C].Proceedings-2007IEEE International Conference on Granular Computing,2007:93-96P
[47]S.Tesfamariam,B.Martin-Perez.Bayesian belief network to assess carbonation-induced corrosion in reinforced concrete[J].Journal of Materials in Civil Engineering,2008,20(11):707-717P
[48]葛哲学.精通Matlab[M].北京:电子工业出版社,2008.2
[49]Duane Hanselman,Bruce Littlefield.精通Matlab7[M].北京:清华大学出版社,2006
[50]FONG T.A survey of socially interactive robots[J].Robotics and Autonomous Systems,2003,42(3-4):143-16P