自主避障系统的研究与设计
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摘要
自主避障的能力是移动机器人智能化程度的重要指标,也是智能型车辆安全行驶的重要保障。自主避障算法是避障功能的灵魂所在,传统的控制算法通常依赖于被控对象精确的数学模型和完整全面的环境信息。当控制过程相对复杂,参数众多,环境信息不全,被控对象的数学模型难以确定时,避障效果往往不能令人满意。
基于上述情况,本文进行了自主避障系统的研究和设计,该系统主要包含前端采集模块,中央处理模块,控制执行模块和被控对象四个部分。前端采集模块包含了超声波测距系统和伪距差分GPS定位系统,分别用于检测行驶过程中障碍物到被控对象的距离以及实现被控对象的自身定位,数据通过RS232串行接口传送给中央处理模块中的上位计算机;中央处理模块根据前端采集到的数据,采用相应的避障控制算法得出控制信号发送给控制执行模块;控制执行模块接收到控制信号后,通过触发无刷电机驱动转向机构,改变被控对象的行驶方向,从而实现避障。
避障控制算法设计上引入了模糊控制理论,将避障行为划分为寻踪和避障两大行为模式,两种行为模式在相应条件下可相互切换。分别建立了两种模式的模糊控制器,对被控对象的转向角进行控制,通过转向角大小控制步长,来实现转弯过程中的自然减速。文中分析了几种典型的陷阱环境,通过设置虚拟目标点的方法进行陷阱避让,给出了具体实现方案。在Visual Basic集成开发环境下,设计了控制仿真平台用于仿真避障导航的控制过程,通过几种典型障碍物环境下的避障实验,验证了该算法具有很好的避障效果。
前端采集模块中完成了基于AT89C51单片机的超声波测距系统的软、硬件设计,并进行了测距实验,结果表明该系统的测量误差为±2%。借助伪距差分GPS定位系统来实现自身的定位,以弥补单纯由传感器检测环境信息的不足。文中介绍了伪距差分GPS的定位原理,以及GPS信号的接收和处理的实现过程。
控制执行机构选用了无刷电机和电动助力转向机构,被控对象采用了智能避障、越障概念车,组成了完整的自主避障系统,设置了真实情况下的不同障碍物环境,进行实车自主避障实验。实验的结果跟仿真的避障导航的控制过程吻合,也验证了该自主避障控制系统具有一定的现实意义。
The ability to avoid the obstacles automatically measures the intellectualiztion of robots, as well as guarante safe travel for intelligent vehicles. Effective algorithm is the core of this ability. Traditional control algorithms always rely on accurate mathematical models of the controlled object and integrity environment information. So they are not quite satisfying, while the control process is relatively complex, or mathematical models are difficult to gain.
During this thesis, the obstacle avoidance system was studied and designed. It could be divided into four parts: Information Gathering Module, Central Processing Module, Controlling Module and Controlled Object. Information Gathering Module was composed by ultrasonic distance measuring system and differential GPS system. They were used to measure the distances between obstacles and controlled object and to realize self-positioning. Datas were sent to upper compouter through serial interface and processed in Central Processing Module.Control signal was sent to Controlling Module. Brushless motor and steering component changed the steering angle in order to avoid obstacles.
Fuzzy Logic Control theory was introduced in algorithm designing.The whole obstacle avoidance process was divided into two main behaivor patterns——goal seeking and obstacle avoidance.These two behavior patterns could be mutully shifted under certain conditions. Fuzzy controllers of each behaviour pattern were established. Step length was controlled by steering angle to decelerate during curving. Hypotesized aim method was used to avoid typical traps. The simulation platform of obstacle avoidance was developped and to testify the validity of the algorithm.
Software and hardware design for ultrasonic distance measuring system were completed based on AT89C51 for Information Gathering Module. Measuring difference was computated to be±2%. Differential GPS was used to make up the insufficiency in circumstance detecting. Meanwhile, the theory of Global Positioning System, the process of signal receiving and processing were introduced.
Brushless motor and Electricl Power Assisted Steering were chosen to compose the Implementing Module.The intelligent concept vehicle was taken as the controlled object to carry out experiments under real circumstances.The results of the experiments matched the simulation process and improved the significance of the whole system.
基于上述情况,本文进行了自主避障系统的研究和设计,该系统主要包含前端采集模块,中央处理模块,控制执行模块和被控对象四个部分。前端采集模块包含了超声波测距系统和伪距差分GPS定位系统,分别用于检测行驶过程中障碍物到被控对象的距离以及实现被控对象的自身定位,数据通过RS232串行接口传送给中央处理模块中的上位计算机;中央处理模块根据前端采集到的数据,采用相应的避障控制算法得出控制信号发送给控制执行模块;控制执行模块接收到控制信号后,通过触发无刷电机驱动转向机构,改变被控对象的行驶方向,从而实现避障。
避障控制算法设计上引入了模糊控制理论,将避障行为划分为寻踪和避障两大行为模式,两种行为模式在相应条件下可相互切换。分别建立了两种模式的模糊控制器,对被控对象的转向角进行控制,通过转向角大小控制步长,来实现转弯过程中的自然减速。文中分析了几种典型的陷阱环境,通过设置虚拟目标点的方法进行陷阱避让,给出了具体实现方案。在Visual Basic集成开发环境下,设计了控制仿真平台用于仿真避障导航的控制过程,通过几种典型障碍物环境下的避障实验,验证了该算法具有很好的避障效果。
前端采集模块中完成了基于AT89C51单片机的超声波测距系统的软、硬件设计,并进行了测距实验,结果表明该系统的测量误差为±2%。借助伪距差分GPS定位系统来实现自身的定位,以弥补单纯由传感器检测环境信息的不足。文中介绍了伪距差分GPS的定位原理,以及GPS信号的接收和处理的实现过程。
控制执行机构选用了无刷电机和电动助力转向机构,被控对象采用了智能避障、越障概念车,组成了完整的自主避障系统,设置了真实情况下的不同障碍物环境,进行实车自主避障实验。实验的结果跟仿真的避障导航的控制过程吻合,也验证了该自主避障控制系统具有一定的现实意义。
The ability to avoid the obstacles automatically measures the intellectualiztion of robots, as well as guarante safe travel for intelligent vehicles. Effective algorithm is the core of this ability. Traditional control algorithms always rely on accurate mathematical models of the controlled object and integrity environment information. So they are not quite satisfying, while the control process is relatively complex, or mathematical models are difficult to gain.
During this thesis, the obstacle avoidance system was studied and designed. It could be divided into four parts: Information Gathering Module, Central Processing Module, Controlling Module and Controlled Object. Information Gathering Module was composed by ultrasonic distance measuring system and differential GPS system. They were used to measure the distances between obstacles and controlled object and to realize self-positioning. Datas were sent to upper compouter through serial interface and processed in Central Processing Module.Control signal was sent to Controlling Module. Brushless motor and steering component changed the steering angle in order to avoid obstacles.
Fuzzy Logic Control theory was introduced in algorithm designing.The whole obstacle avoidance process was divided into two main behaivor patterns——goal seeking and obstacle avoidance.These two behavior patterns could be mutully shifted under certain conditions. Fuzzy controllers of each behaviour pattern were established. Step length was controlled by steering angle to decelerate during curving. Hypotesized aim method was used to avoid typical traps. The simulation platform of obstacle avoidance was developped and to testify the validity of the algorithm.
Software and hardware design for ultrasonic distance measuring system were completed based on AT89C51 for Information Gathering Module. Measuring difference was computated to be±2%. Differential GPS was used to make up the insufficiency in circumstance detecting. Meanwhile, the theory of Global Positioning System, the process of signal receiving and processing were introduced.
Brushless motor and Electricl Power Assisted Steering were chosen to compose the Implementing Module.The intelligent concept vehicle was taken as the controlled object to carry out experiments under real circumstances.The results of the experiments matched the simulation process and improved the significance of the whole system.
引文
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[4]C.C.wong,M.F.Chou,C.P.Hwang,A Method for Obstacle Avoidance and Shooting Active of the Robot Soccer,ICRA,2001:3778～3782.
[5]朱咏杰,王树国,一种移动机器人路径规划算法的性能仿真分析与参数优化,机器人,2001,23(10):7～9
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[20]马兆青,袁曾任,基于栅格方法的移动机器人实时导航和避障,机器人,1996,(11):344～348
[21]李玲,邹大勇,基于自适应模糊人工势场法的自动引导小车路径规划,长沙电力学院学报,2005,20(3)
[22]赵新华,曹作良,可移动机器人的运动学模型与控制理论,机器人,1994,16(4):215～218
[23]温素芳,基于模糊控制器的移动机器人路径规划研究,应用科技,2005,32(4):30~33
[24]蒋再男,基于模糊控制的未知环境下移动机器人导航技术研究,[哈尔滨工业大学硕士学位论文],哈尔滨,哈尔滨工业大学,2005
[25]孟庆浩,彭商贤,使用非视觉传感器的移动机器人定位问题的研究方法,中国机械工程,1998,9(2):49～52
[26]黄素平,何清华,一种移动机器人路径规划方法,机床与液压,2004,(5):45～46
[27]卢晓军,一种基于自由空间法的虚拟人行走规划方法,计算机工程与科学,2005,(8)
[28]C. C. Lee,Fuzzy logic in control systems: Fuzzy logic controller-partII,IEEE Trans. Syst.,Man,Cybern.,1990,20(2):419～435
[29]O .Khatib,Real-time Obstacle Avoidance for Manipulators and Mobile Robots, Int. J Robotics Research,1986,5 (1): 90～98
[30]B.S.Ryu,H.S.Yang,Integration of Reactive Behaviors and Enhanced Topological Map for Robust Mobile Robot Navigation,IEEE Trans.on SMC-Part A,1999,29(5):474～485
[31]胡寿松,自动控制原理(第四版),北京,科学出版社,2001
[32]张其善,吴今培,杨东凯,智能车辆定位导航系统及应用,北京,科学出版社,2002
[33]常青,杨东凯,寇艳红,车辆导航定位方法及应用,北京,机械工业出版社,2005
[35]李光明,Visual Basic 6.0,北京,冶金工业出版社,2001
[36]魏江江,林少景,Visual Basic基础编程百例,北京,清华大学出版社,2001
[37]陈超超,轮式移动机器人避障的研究,[燕山大学硕士学位论文],燕山,燕山大学,2004
[38]徐国华, 谭民.移动机器人的发展现状及其趋势.机器人技术与应用,2001,3:5～16
[39]刘凤然, 田红芳,王侃,基于单片机的移动机器人自动避障控制系统.中国仪器仪 表,2001,3:27～29
[40]LEE S, A fuzzy navigation system for mobile construction robots, Automation in Construction, 1997,(6) :97～107
[41]Charles.C.Chan, K.T.Song,Ultrasonic Sensor Data Integration and Its Application to Environment Perception,Journal of Robotic Systems,1996,13(10): 663～677
[42]A.Elfes,Sonar-Based Real-World Mapping and Navigation,IEEE Trans,Robotics and Automation,1978,3(3):249～265
[43]A.Zelinsky,Mobile Robot Map Making Using Sonar Robotic Systems ,1991,8(5):557～577
[44]J.L.Crowley,World Modeling and Position Estimation for A Mobile Robot Using Ultrasonic Ranging,Proc.IEEE int.Conf.Robotics and Automation, Scottsdale,AZ,1989:674～680
[45]J.Borenstein, Y.Koren.Obstacle Avoidance Using Ultrasonic Sensors,IEEE Trans,Robotics and Automation,1988,4(2):213～218
[46]Daehee Kang, R.C.Luo,Position Estimation for Mobile Robot Using Sensor Fusion.Proc,IEEE Conf.on Multisensor Fusion and Integration for Intelligent System,1994,10:647～652
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[49]J.Borenstein, Y.Koren.Histogramic In-Motion Mapping for Mobile Robot Obstacle Avoidance,IEEE Trans,Robotics and Automation,1991,7(4):535～539
[50]Ching chang Wong,A Method for Obstacle Avoidance and Shooting Action of the Robot Soccer,IEEE International Conference on Robotics & Automation,2001(5)
[51]LEE LI, Fuzzy motion planning of mobile robots in unkonwn environments,Journal of Intelligent and Robotic Systenms, 2003,(37):177～191
[2]郑向阳,熊蓉,顾大强,移动机器人导航技术现状与发展,机器人,2003,20(5):35～37
[3]任志阳,移动机器人控制器的研究及其实现,[南京理工大学硕士学位论文],南京,南京理工大学,2004
[4]C.C.wong,M.F.Chou,C.P.Hwang,A Method for Obstacle Avoidance and Shooting Active of the Robot Soccer,ICRA,2001:3778～3782.
[5]朱咏杰,王树国,一种移动机器人路径规划算法的性能仿真分析与参数优化,机器人,2001,23(10):7～9
[6]孙增圻,智能控制理论与技术,北京,清华大学出版社,1997
[7]王耀南,智能控制系统,长沙,湖南大学出版社,1996
[8]窦振中,模糊逻辑控制技术及应用,北京,北京航空航天大学出版社,1995:156
[9]赵振宇,徐用懋,模糊理论和神经网络的基础与应用,北京,清华大学出版社,1996
[10]楼顺天,基于MATLAB的系统分析与设计-模糊系统,西安,西安电子科技大学出版社,2001
[11]石剑民,崔建国,手持式GPS接收机的设计,沈阳航空工业学院学报,2005,22(2)54~57
[12]刘基余,GPS卫星导航定位原理与方法,北京,科学出版社,2003
[13]冯宝红,叶松,GPS 伪距差分解算改进模型的研究 ,南京工业大学学报,2005,27(7)
[14]黄智伟,GPS接收机电路设计,北京,国防工业出版社,2005
[15]任勇益,自动导向车控制系统的数学模型与应用,国防科技大学学报,1997, 19(6)
[16]胡晓敏,张友军,移动机器人导航模型的设计方法,机器人,1997,19(7)
[17]李庆中, 顾伟康, 叶秀清.基于遗传算法的移动机器人动态避障路径规划方法,模 式识别与人工智能,2002,15(2):161～166
[18]李冬辉,FUZZY控制规则调整和FUZZY控制系统寻优及其仿真研究,模糊数学,1986(3):53～61
[19]杜忠达,自主移动机器人在局部未知环境下路径规划和避障问题的研究,[上海交通大学硕士学位论文],上海,上海交通大学,1998
[20]马兆青,袁曾任,基于栅格方法的移动机器人实时导航和避障,机器人,1996,(11):344～348
[21]李玲,邹大勇,基于自适应模糊人工势场法的自动引导小车路径规划,长沙电力学院学报,2005,20(3)
[22]赵新华,曹作良,可移动机器人的运动学模型与控制理论,机器人,1994,16(4):215～218
[23]温素芳,基于模糊控制器的移动机器人路径规划研究,应用科技,2005,32(4):30~33
[24]蒋再男,基于模糊控制的未知环境下移动机器人导航技术研究,[哈尔滨工业大学硕士学位论文],哈尔滨,哈尔滨工业大学,2005
[25]孟庆浩,彭商贤,使用非视觉传感器的移动机器人定位问题的研究方法,中国机械工程,1998,9(2):49～52
[26]黄素平,何清华,一种移动机器人路径规划方法,机床与液压,2004,(5):45～46
[27]卢晓军,一种基于自由空间法的虚拟人行走规划方法,计算机工程与科学,2005,(8)
[28]C. C. Lee,Fuzzy logic in control systems: Fuzzy logic controller-partII,IEEE Trans. Syst.,Man,Cybern.,1990,20(2):419～435
[29]O .Khatib,Real-time Obstacle Avoidance for Manipulators and Mobile Robots, Int. J Robotics Research,1986,5 (1): 90～98
[30]B.S.Ryu,H.S.Yang,Integration of Reactive Behaviors and Enhanced Topological Map for Robust Mobile Robot Navigation,IEEE Trans.on SMC-Part A,1999,29(5):474～485
[31]胡寿松,自动控制原理(第四版),北京,科学出版社,2001
[32]张其善,吴今培,杨东凯,智能车辆定位导航系统及应用,北京,科学出版社,2002
[33]常青,杨东凯,寇艳红,车辆导航定位方法及应用,北京,机械工业出版社,2005
[35]李光明,Visual Basic 6.0,北京,冶金工业出版社,2001
[36]魏江江,林少景,Visual Basic基础编程百例,北京,清华大学出版社,2001
[37]陈超超,轮式移动机器人避障的研究,[燕山大学硕士学位论文],燕山,燕山大学,2004
[38]徐国华, 谭民.移动机器人的发展现状及其趋势.机器人技术与应用,2001,3:5～16
[39]刘凤然, 田红芳,王侃,基于单片机的移动机器人自动避障控制系统.中国仪器仪 表,2001,3:27～29
[40]LEE S, A fuzzy navigation system for mobile construction robots, Automation in Construction, 1997,(6) :97～107
[41]Charles.C.Chan, K.T.Song,Ultrasonic Sensor Data Integration and Its Application to Environment Perception,Journal of Robotic Systems,1996,13(10): 663～677
[42]A.Elfes,Sonar-Based Real-World Mapping and Navigation,IEEE Trans,Robotics and Automation,1978,3(3):249～265
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