基于LIN的移动机器人运动控制机理研究
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摘要
移动机器人是目前机器人领域研究的重点之一,吸引着众多国内外学者的参与,它有着巨大的应用潜力。低成本的LIN总线通信方法,为移动机器人上各种电气设备之间的互联提供了有效的解决途径,可以应用到工业和生活中的电气设备互联中。
本论文对“基于LIN的移动机器人运动控制机理”进行了研究,主要包括:介绍了移动服务机器人的国内外发展现状,指出移动机器人研究领域的几个关键技术;对移动机器人的稳定性、机动性和可控性进行了分析,建立了常用轮子的运动学约束方程,进一步建立了移动机器人的运动学模型,并在此基础上对移动机器人进行了三维建模,完成了移动机器人样机的装配;对基于神经网络的移动机器人路径规划技术和移动机器人路径跟踪模糊控制技术进行了研究,提出使用模拟退火法进行最优路径求解和二次曲线跟踪法进行快速纠偏的方案;系统深入地阐述了LIN通信原理,创造性的将LIN通信技术应用到移动机器人上,为移动机器人内部网络互连提供了一种低成本解决方案;设计和制作了基于LIN的直流电机驱动控制系统和基于LIN的移动机器人路径跟踪模糊控制系统,首次将KMA200可编程角度传感器应用到电机测速中,将改进后的直流电机作为移动机器人的驱动电机;设计制作了必要的硬件和软件装置,对两个控制系统分别进行了功能调试,在成功调试的基础上,分别设计了两个控制系统的软件。
研究表明,基于LIN的移动机器人控制技术是可行的,具有很高的使用价值和广阔的发展前景。
The research on mobile robot is a hotspot in robotics. It has attracted the participation of lots of researches. Mobile robot has huge applications in the future. A low cost LIN communication is an effective solution for electrical equipment interconnection in mobile robot and industry.
This paper aims at mobile robot motion control study based on LIN communication. It introduced the development situation of mobile and service robot, pointed out the key technologies in mobile robotics. It analyzed stability, mobility and controllability of the mobility robot, studied wheels kinematics constraints and established differential drive mobile robot kinematics model. Based on these studies above, a 3D model was established, and a mobile robot sample was assembled. It did research of path planning technology and path tracking technology for the mobile robot. The path planning technology is based on neural network and the path tracking technology is based on fuzzy logical controlling. It proposed that simulate annealing algorithm is well for finding the best path and conic tracking controlling is well for fleetly correcting the pose error. This paper systematically and deeply discusses LIN communication theory, applies LIN communication technology to mobile robot in a creative way, and provides a low cost solution for mobile robot network interconnect. We designed and produced a driving and controlling system for DC motor and a fuzzy logical controller system for path tracking, which is based on LIN communication technology, firstly applied KMA200 programmable angle sensor to measure motor speed, and used an improved DC motor for the mobile robot driving. We designed and produced some necessary hardware and software as well, which are going to debug the function models of the tow control systems. Based on the successful debug, we design the whole software for the two control systems.
The research shows that mobile control technology which is based on LIN communication is feasible and has very high use value and bright future.
本论文对“基于LIN的移动机器人运动控制机理”进行了研究,主要包括:介绍了移动服务机器人的国内外发展现状,指出移动机器人研究领域的几个关键技术;对移动机器人的稳定性、机动性和可控性进行了分析,建立了常用轮子的运动学约束方程,进一步建立了移动机器人的运动学模型,并在此基础上对移动机器人进行了三维建模,完成了移动机器人样机的装配;对基于神经网络的移动机器人路径规划技术和移动机器人路径跟踪模糊控制技术进行了研究,提出使用模拟退火法进行最优路径求解和二次曲线跟踪法进行快速纠偏的方案;系统深入地阐述了LIN通信原理,创造性的将LIN通信技术应用到移动机器人上,为移动机器人内部网络互连提供了一种低成本解决方案;设计和制作了基于LIN的直流电机驱动控制系统和基于LIN的移动机器人路径跟踪模糊控制系统,首次将KMA200可编程角度传感器应用到电机测速中,将改进后的直流电机作为移动机器人的驱动电机;设计制作了必要的硬件和软件装置,对两个控制系统分别进行了功能调试,在成功调试的基础上,分别设计了两个控制系统的软件。
研究表明,基于LIN的移动机器人控制技术是可行的,具有很高的使用价值和广阔的发展前景。
The research on mobile robot is a hotspot in robotics. It has attracted the participation of lots of researches. Mobile robot has huge applications in the future. A low cost LIN communication is an effective solution for electrical equipment interconnection in mobile robot and industry.
This paper aims at mobile robot motion control study based on LIN communication. It introduced the development situation of mobile and service robot, pointed out the key technologies in mobile robotics. It analyzed stability, mobility and controllability of the mobility robot, studied wheels kinematics constraints and established differential drive mobile robot kinematics model. Based on these studies above, a 3D model was established, and a mobile robot sample was assembled. It did research of path planning technology and path tracking technology for the mobile robot. The path planning technology is based on neural network and the path tracking technology is based on fuzzy logical controlling. It proposed that simulate annealing algorithm is well for finding the best path and conic tracking controlling is well for fleetly correcting the pose error. This paper systematically and deeply discusses LIN communication theory, applies LIN communication technology to mobile robot in a creative way, and provides a low cost solution for mobile robot network interconnect. We designed and produced a driving and controlling system for DC motor and a fuzzy logical controller system for path tracking, which is based on LIN communication technology, firstly applied KMA200 programmable angle sensor to measure motor speed, and used an improved DC motor for the mobile robot driving. We designed and produced some necessary hardware and software as well, which are going to debug the function models of the tow control systems. Based on the successful debug, we design the whole software for the two control systems.
The research shows that mobile control technology which is based on LIN communication is feasible and has very high use value and bright future.
引文
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[8]陶晔.组合模块化机器人机械手爪的设计研究[D].南京:东南大学硕士学位论文,2004,03.
[9]丁伟,孙华,曾建辉.基于多传感器信息融合的移动机器人导航综述[J].传感器与微系统,2006,25(7):1-3.
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[12]刘华军,杨静宁,陆建峰等.移动机器人运动规划研究综述[J].中国工程科学,2006,8(1):85-94.
[13]王仲民,刘继岩,岳宏.移动机器人自主导航技术研究综述[J].天津职业技术师范学院学报,2004,12(4):11-15.
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[17]A.R.S.A.Alamdari.Unknown Environment Representation for Mobile Robot Using Spiking Neural Networks[J].Transactions on Engineering,Computing and Technology,V6 June 2005:49-52.
[18]赵慧.基于激光雷达和神经网络的移动机器人综合局部路径规划[D].中南大学硕士学位论 文,2004.
[19]Z.HENDZEL.Collision free path planning and control of wheeled mobile robot using Kohonen self-organising map[J].Bulletin of the polish academy of sciences technical sciences,Vol.53,No.1,2005:39-47.
[20]ZHU Yongjie,CHANG jiang,WANG shuguo.A new path-planning algorithm for mobile robot based on neural network[J].Proceeding of IEEE TENCON,2002.
[21]S.K.Kim,A.M.ASCE,J.S.Russell,et al.Construction Robot Path-Planning for Earthwork Operations[J].Journal of Computing in Civil Engineering,2003:97-104.
[22]S.Se,D.Lowe,J.Little.Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks[J].The International Journal of Robotics Research,August 2002:738-758.
[23]Dominic L,F Michaud,J M Valin.Autonomous Mobile Robot That Can Read[J].EURASIP Journal on Applied Signal Processing 2004:17,2650-2662.
[24]Q.H.Wang,T.Ivanov,P.Aarabi.Acoustic robot navigation using distributed microphone arrays[J].Information Fusion 5,2004:131-140.
[25]N.Harper,P.McKerrow.Recognising plants with ultrasonic sensing for mobile robot navigation[J].Robotics and Autonomous Systems 34,2001:71-82.
[26]M.Betke,L.Gurvits.Mobile Robot Localization Using Landmarks[J].IEEE Transaction on Robotics and Automation,VOL.13,NO.2,APRIL 1997:251-263.
[27]H.A.Awad,M.A.Al-Zorkany.Mobile Robot Navigation Using Local Model Networks[J].Transactions on Engineering,Computing and Technology,Vol.1 December 2004:326-331.
[28]P.G.Zavlangas,P.S.G.Tzafestas,Dr.K.Althoefer.Fuzzy Obstacle Avoidance and Navigation for Omnidirectional Mobile Robots[J].ESIT 2000,14-15 September 2000,Aachen,Germany:375-382.
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