室外自主移动机器人导航系统研究与设计
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摘要
自主式移动机器人具有高度自规划、自组织和自适应能力,适合于在复杂的非结构化环境中工作。其目标是在没有人的干预、无需对环境做任何规定和改变的条件下有目的地移动和完成相应任务。所以,在自主式移动机器人相关技术中,导航是其中一个重要组成部分,也是移动机器人实现智能化以至完全自主的关键技术。我国在智能移动机器人研究方面虽然已经取得了一定的成果,但由于起步较晚,在研究和应用方面都落后于一些西方国家,而且还没有达到完全实用。因此,进行这项研究,具有一定的理论意义和工程应用意义。
移动机器人导航系统主要内容包括移动机器人的定位和路径规划。为了赋予机器人智能和自主的能力,本文对移动机器人导航系统进行了分析研究。
首先介绍了移动机器人的发展状况,分析了国内外移动机器人研究现状。并综合分析比较了目前常用的移动机器人定位和路径规划技术,结合设计需求,提出了室外自主移动机器人定位及路径规划比较合理的总体设计方案。
然后根据实验需要编写移动机器人控制程序,以便于方便地应用导航算法。在控制软件的编写过程中,充分考虑了机器人硬件平台可变的因素,创新性的将设计模式引入,使程序可以很好地应对变化,并且尽量使用了跨平台技术,为将来的平台移植做好了准备。
针对定位本文分析研究了GPS定位,DR导航原理。GPS定位存在误差大,有盲区等缺点;而DR航位推算存在初始准确但误差随时间累积的问题。因此,对移动机器人运动进行建模并使用卡尔曼滤波将二者数据进行融合,综合二者的优点,可以有效提高定位精度。通过实验表明,滤波后定位效果大大好于单独使用GPS或者DR定位。
针对路径规划本文分析研究了基于行为的运动控制算法,将其应用于室外移动机器人的导航控制中。借鉴免疫网络的原理,构建了一个行为仲裁系统,对行为单元进行协调。系统能根据外部环境动态地改变行为单元的优先级,同时考虑到反应式控制体系固有的缺陷,添加规划推理模块对系统进行推理和指导,增强系统对环境的适应能力和灵活性。为了验证路径规划算法的可行性,使用VC++编写了仿真软件并对路径规划算法进行了验证。实验结果表明,在比较理想的情况下,算法可以很好地完成机器人导航任务。在仿真的基础上,使用硬件平台对算法进行验证,并分析了实验结果。
最后对全文工作进行总结,并对研究工作的继续深入提出设想。
Autonomous mobile robot has the ability of self-planning, self -organization and adaptivity. Its goal is moving and accomplishing tasks without human assistance or changing environment. So navigation is an important part of mobile robot field. And it is a key technology of mobile robot intelligence. Although our country has made some achievements, we still fall behind some western countries. And we have few robots applied in the real world. So it is of great theoretic and engineering importance to do this research.
The main content of mobile navigation system contains mobile robot localization and path-planning. In order to enable mobile robot the abilities of aptitude and autonomy, the mobile robot navigation system is analysed and researched.
First of all, the developing history of mobile robot is introduced and the developing status of mobile robot in domestic and overseas is analysed. The main technologies of mobile robot localization and path-planning nowadays is studied . Based on the requirement, a reasonable overall design of outdoor autonomous mobile robot localization and path-planning is presented.
Then a mobile robot control program is written to apply the algorithms of navigation conveniently. The design patterns is applied in the program innovatively so the program can cope with changes easily and it is ready for running in another operating system.
After that GPS localization and DR navigation principle are analysed and researched. The error of GPS localization is large and there are areas where the GPS data cannot be received. The error of DR is initially small but will be larger and larger as the time goes. So the Kalman filter is used to combine the GPS data and DR data to integrate their advantages. It is shown by the experiment, the filter improves the localization precision greatly.
Then the behavior based motion control method is analysed and is applied in the outdoor mobile robot navigation. By using immune network principle, a behavior arbitration system is built to select behavior. The priority of behavior can be dynamically changed depending on the outside circumstances. The drawback of reactive controlling system is considered, and the planning or reasoning module is added to enhance the system’s adaptation to environment and flexibility. In order to verify the feasibility of the algorithm, a simulation program is designed in Visual C++ and the path-planning algorithm is evaluated. Experiments demonstrate that in relatively ideal condition the robot can accomplish task using the algorithm. Then the algorithm is tested on the hardware platform and the result is analysed.
In the end, a summary is made for the whole research work and the future research plan is presented.
移动机器人导航系统主要内容包括移动机器人的定位和路径规划。为了赋予机器人智能和自主的能力,本文对移动机器人导航系统进行了分析研究。
首先介绍了移动机器人的发展状况,分析了国内外移动机器人研究现状。并综合分析比较了目前常用的移动机器人定位和路径规划技术,结合设计需求,提出了室外自主移动机器人定位及路径规划比较合理的总体设计方案。
然后根据实验需要编写移动机器人控制程序,以便于方便地应用导航算法。在控制软件的编写过程中,充分考虑了机器人硬件平台可变的因素,创新性的将设计模式引入,使程序可以很好地应对变化,并且尽量使用了跨平台技术,为将来的平台移植做好了准备。
针对定位本文分析研究了GPS定位,DR导航原理。GPS定位存在误差大,有盲区等缺点;而DR航位推算存在初始准确但误差随时间累积的问题。因此,对移动机器人运动进行建模并使用卡尔曼滤波将二者数据进行融合,综合二者的优点,可以有效提高定位精度。通过实验表明,滤波后定位效果大大好于单独使用GPS或者DR定位。
针对路径规划本文分析研究了基于行为的运动控制算法,将其应用于室外移动机器人的导航控制中。借鉴免疫网络的原理,构建了一个行为仲裁系统,对行为单元进行协调。系统能根据外部环境动态地改变行为单元的优先级,同时考虑到反应式控制体系固有的缺陷,添加规划推理模块对系统进行推理和指导,增强系统对环境的适应能力和灵活性。为了验证路径规划算法的可行性,使用VC++编写了仿真软件并对路径规划算法进行了验证。实验结果表明,在比较理想的情况下,算法可以很好地完成机器人导航任务。在仿真的基础上,使用硬件平台对算法进行验证,并分析了实验结果。
最后对全文工作进行总结,并对研究工作的继续深入提出设想。
Autonomous mobile robot has the ability of self-planning, self -organization and adaptivity. Its goal is moving and accomplishing tasks without human assistance or changing environment. So navigation is an important part of mobile robot field. And it is a key technology of mobile robot intelligence. Although our country has made some achievements, we still fall behind some western countries. And we have few robots applied in the real world. So it is of great theoretic and engineering importance to do this research.
The main content of mobile navigation system contains mobile robot localization and path-planning. In order to enable mobile robot the abilities of aptitude and autonomy, the mobile robot navigation system is analysed and researched.
First of all, the developing history of mobile robot is introduced and the developing status of mobile robot in domestic and overseas is analysed. The main technologies of mobile robot localization and path-planning nowadays is studied . Based on the requirement, a reasonable overall design of outdoor autonomous mobile robot localization and path-planning is presented.
Then a mobile robot control program is written to apply the algorithms of navigation conveniently. The design patterns is applied in the program innovatively so the program can cope with changes easily and it is ready for running in another operating system.
After that GPS localization and DR navigation principle are analysed and researched. The error of GPS localization is large and there are areas where the GPS data cannot be received. The error of DR is initially small but will be larger and larger as the time goes. So the Kalman filter is used to combine the GPS data and DR data to integrate their advantages. It is shown by the experiment, the filter improves the localization precision greatly.
Then the behavior based motion control method is analysed and is applied in the outdoor mobile robot navigation. By using immune network principle, a behavior arbitration system is built to select behavior. The priority of behavior can be dynamically changed depending on the outside circumstances. The drawback of reactive controlling system is considered, and the planning or reasoning module is added to enhance the system’s adaptation to environment and flexibility. In order to verify the feasibility of the algorithm, a simulation program is designed in Visual C++ and the path-planning algorithm is evaluated. Experiments demonstrate that in relatively ideal condition the robot can accomplish task using the algorithm. Then the algorithm is tested on the hardware platform and the result is analysed.
In the end, a summary is made for the whole research work and the future research plan is presented.
引文
[1]金周英.关于我国智能机器人发展的几点思考.机器人技术与应用,2001,(4):527.
[2]孟庆春,齐勇,张淑军等.智能机器人及其发展.中国海洋大学学报,2004,34(5):831～838.
[3]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):7～14.
[4] Richard Volpe,Tara Estlin,Sharon Laubach,Clark Olson,J.Balaram. Enhanced Mars Rover Navigation Techniques. Proceeding of the IEEE International Conference on Robotics and Automation. 2000(4):926～931.
[5] Sharon Lynn Laubach. Theory and Experiments in Autonomous Sensor-based Motion Planning with Applications for Flight Planetary Microrovers. Thesis for the Degree of Doctor of Philosophy.
[6]欧青立,何克忠.室外智能移动机器人的发展及其关键技术研究.机器人. 2000,22(6).
[7]张颖,吴成东,原宝龙.机器人路径规划方法综述.控制工程,2003,10(5): 152～153.
[8]李磊,叶涛,谭民等.移动机器人技术研究现状与未来.机器人,2002,24(5): 475～477.
[9]赵清杰,连广宇,孙增圻.机器人视觉伺服综述.控制与决策,2001,16(6): 851～852.
[10]王军,苏剑波,席裕庚.多传感器集成与融合概述.机器人,2001,23(2): 183～192.
[11]陈华志,谢存禧,曾德怀.多传感器信息融合在移动机器人导航中的应用.组合机床与自动化加工技术,2003,8(9): 14～15.
[12]王超越.多机器人协作系统研究.哈尔滨工业大学博士论文,1999.
[13]孟伟,洪炳,韩学东.一种多机器人协作控制方法.机器人,2004,26(1): 58～59.
[14] H.F Durrant Whyte. Where am I? A tutorial on mobile vehicle localization. Industrial Robot, 1994.21(2):11～16.
[15]李群明,熊蓉,褚健.室内自主移动机器人定位方法研究综述.机器人, 2003,25(6):560～563.
[16] Borenstein J, Feng L. Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation, 1996, 12(6): 869～880.
[17] Matthew Ridley, Eric Nettleton, Salah Dukkarieh et al. Tracking in Decentralized Air-Ground Sensing Networks.In:ISIF 2002:616～623.
[18] Mardello Restelli, Domenico G , Sorrenti and Fabio Metal. A Robot LocalizationMethod Based on Evidence Accumulation and Multi-Resolution.In: Proc Of The 2002 IEEE/RSJ Inti.Conference On Intelligent Robots And Systems. Lausanne, Switzerland.2002:415～420.
[19] Gutman JS, Schlegel C.Amos. Comparison of Scan Matching Approaches for Self-Localization in Indoor Environments.In:Proc of The 1st Euromicro Workshop on Advanced Mobile Robots.Kaiserslautern,Germany,IEEE Computer Society Press,1996:231～237.
[20] R.西格沃特,I.R.诺巴克什.自主移动机器人导论.西安交通大小出版社,2006
[21]李瑞峰,李伟招.基于多传感器信息融合的移动机器人路径规划.机电一体化,2002(4).
[22]艾海舟,张钹.基于拓扑的路径规划问题的图形解法.机器人,1990,12(5):20～24.
[23] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. The1985 IEEE International Conference on Robotics and Automation, St. Louis, 1985:500～505.
[24]仲欣,吕恬生.基于遗传算法的汽车式移动机器人路径规划方法.上海交通大学学报,1999,33(7):885～888.
[25]崔茂源等.基于模糊逻辑的自主移动机器人实时滚动路径规划及控制.吉林工业大学自然科学学报,1999,29(1):58～63.
[26]孙羽等.神经网络在智能机器人导航系统中的应用.计算机工程,2002,28(1):138～140.
[27]博创机器人实验参考手册.
[28] Erich Gamma等.设计模式:可复用面向对象软件的基础.机械工业出版社,2000.
[29] Joshua Kerievsky.重构与模式.人民邮电出版社,2006.
[30] Craig Larman.UML和模式应用.机械工业出版社,2006.
[31] Brain Hook.跨平台软件开发——C&C++.清华大学出版社,2006.
[32]李天文.GPS导航原理与应用.科学出版社,2004.
[33]龚健伟等.Visual C++/Turbo C串口通信编程实践.电子工业出版社,2004.
[34]付梦印等.Kalman滤波理论及其在导航系统中的应用.科学出版社,2003.
[35] Greg Welch,Gary Bishop. An Introduction to the Kalman Filter. SIGGRAPH 2001 Course 8
[36]孙希延等.卡尔曼滤波实现车载GPS/DR组合导航.电子技术应用,2006,11:149～152.
[37]万德钧等.GPS动态滤波的理论,方法及其应用.江苏科学技术出版社,2003.
[38]孙勇智.人工免疫系统模型、算法及其应用研究[博士学位论文].浙江大学,2004.
[39]焦李成等.人工免疫计算、学习与识别.科学出版社,2005.
[40] Akio Ishiguro. A Robot with a Decentralized Consensus-making Mechanism Based on the Immune System, In Proc.ISADS'97:231～237.
[41]刘克胜等.一种基于免疫原理的自律机器人行为控制算法.计算机工程与应用2000,5:30～32.
[42]王孙安等.移动机器人路径发现与规划的免疫算法.系统仿真学报,2002,8:995～997.
[43] Ricky.编写移动机器人仿真软件. http://www.roboticfan.com/college/tutorial/200610/255.shtml
[44] Robert C.Martin .敏捷软件开发——原则、模式与实践.清华大学出版社,2003.
[45]罗攀等.基于面向对象的人工免疫系统模型的多传感器融合.机器人技术与应用, 2002,1:22～27.
[46] Dong-Wook Lee. Artificial Immune Network-based Cooperative Control in Collective Autonomous Mobile Robots.6th IEEE International Workshop on Robot and Human Communication,58～63.
[47] J.H.Jun, Dong-Wook Lee.Realization of Cooperative Strategies and Swarm Behavior in Distributed Autonomous Robotic Systems Using Artificial Immune System.1999 IEEE International Conference on Systems,Man and Cybernetics, Oct 12-15,Tokyo,Japan,1999,(4),614～619.
[48]段清娟,王润孝.机器人学中应用的人工免疫原理、模型与展望.机器人,2005,9(27), 475～480.
[49] Philipp Althaus. Indoor Navigation for Mobile Robots: Control and Representations[D]. Royal Institute of Technology,2003.
[2]孟庆春,齐勇,张淑军等.智能机器人及其发展.中国海洋大学学报,2004,34(5):831～838.
[3]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):7～14.
[4] Richard Volpe,Tara Estlin,Sharon Laubach,Clark Olson,J.Balaram. Enhanced Mars Rover Navigation Techniques. Proceeding of the IEEE International Conference on Robotics and Automation. 2000(4):926～931.
[5] Sharon Lynn Laubach. Theory and Experiments in Autonomous Sensor-based Motion Planning with Applications for Flight Planetary Microrovers. Thesis for the Degree of Doctor of Philosophy.
[6]欧青立,何克忠.室外智能移动机器人的发展及其关键技术研究.机器人. 2000,22(6).
[7]张颖,吴成东,原宝龙.机器人路径规划方法综述.控制工程,2003,10(5): 152～153.
[8]李磊,叶涛,谭民等.移动机器人技术研究现状与未来.机器人,2002,24(5): 475～477.
[9]赵清杰,连广宇,孙增圻.机器人视觉伺服综述.控制与决策,2001,16(6): 851～852.
[10]王军,苏剑波,席裕庚.多传感器集成与融合概述.机器人,2001,23(2): 183～192.
[11]陈华志,谢存禧,曾德怀.多传感器信息融合在移动机器人导航中的应用.组合机床与自动化加工技术,2003,8(9): 14～15.
[12]王超越.多机器人协作系统研究.哈尔滨工业大学博士论文,1999.
[13]孟伟,洪炳,韩学东.一种多机器人协作控制方法.机器人,2004,26(1): 58～59.
[14] H.F Durrant Whyte. Where am I? A tutorial on mobile vehicle localization. Industrial Robot, 1994.21(2):11～16.
[15]李群明,熊蓉,褚健.室内自主移动机器人定位方法研究综述.机器人, 2003,25(6):560～563.
[16] Borenstein J, Feng L. Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation, 1996, 12(6): 869～880.
[17] Matthew Ridley, Eric Nettleton, Salah Dukkarieh et al. Tracking in Decentralized Air-Ground Sensing Networks.In:ISIF 2002:616～623.
[18] Mardello Restelli, Domenico G , Sorrenti and Fabio Metal. A Robot LocalizationMethod Based on Evidence Accumulation and Multi-Resolution.In: Proc Of The 2002 IEEE/RSJ Inti.Conference On Intelligent Robots And Systems. Lausanne, Switzerland.2002:415～420.
[19] Gutman JS, Schlegel C.Amos. Comparison of Scan Matching Approaches for Self-Localization in Indoor Environments.In:Proc of The 1st Euromicro Workshop on Advanced Mobile Robots.Kaiserslautern,Germany,IEEE Computer Society Press,1996:231～237.
[20] R.西格沃特,I.R.诺巴克什.自主移动机器人导论.西安交通大小出版社,2006
[21]李瑞峰,李伟招.基于多传感器信息融合的移动机器人路径规划.机电一体化,2002(4).
[22]艾海舟,张钹.基于拓扑的路径规划问题的图形解法.机器人,1990,12(5):20～24.
[23] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. The1985 IEEE International Conference on Robotics and Automation, St. Louis, 1985:500～505.
[24]仲欣,吕恬生.基于遗传算法的汽车式移动机器人路径规划方法.上海交通大学学报,1999,33(7):885～888.
[25]崔茂源等.基于模糊逻辑的自主移动机器人实时滚动路径规划及控制.吉林工业大学自然科学学报,1999,29(1):58～63.
[26]孙羽等.神经网络在智能机器人导航系统中的应用.计算机工程,2002,28(1):138～140.
[27]博创机器人实验参考手册.
[28] Erich Gamma等.设计模式:可复用面向对象软件的基础.机械工业出版社,2000.
[29] Joshua Kerievsky.重构与模式.人民邮电出版社,2006.
[30] Craig Larman.UML和模式应用.机械工业出版社,2006.
[31] Brain Hook.跨平台软件开发——C&C++.清华大学出版社,2006.
[32]李天文.GPS导航原理与应用.科学出版社,2004.
[33]龚健伟等.Visual C++/Turbo C串口通信编程实践.电子工业出版社,2004.
[34]付梦印等.Kalman滤波理论及其在导航系统中的应用.科学出版社,2003.
[35] Greg Welch,Gary Bishop. An Introduction to the Kalman Filter. SIGGRAPH 2001 Course 8
[36]孙希延等.卡尔曼滤波实现车载GPS/DR组合导航.电子技术应用,2006,11:149～152.
[37]万德钧等.GPS动态滤波的理论,方法及其应用.江苏科学技术出版社,2003.
[38]孙勇智.人工免疫系统模型、算法及其应用研究[博士学位论文].浙江大学,2004.
[39]焦李成等.人工免疫计算、学习与识别.科学出版社,2005.
[40] Akio Ishiguro. A Robot with a Decentralized Consensus-making Mechanism Based on the Immune System, In Proc.ISADS'97:231～237.
[41]刘克胜等.一种基于免疫原理的自律机器人行为控制算法.计算机工程与应用2000,5:30～32.
[42]王孙安等.移动机器人路径发现与规划的免疫算法.系统仿真学报,2002,8:995～997.
[43] Ricky.编写移动机器人仿真软件. http://www.roboticfan.com/college/tutorial/200610/255.shtml
[44] Robert C.Martin .敏捷软件开发——原则、模式与实践.清华大学出版社,2003.
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