近海智能探测机器人软硬件实现及导航算法研究
摘要
目前,智能机器人正在被广泛应用在搜索、援救、军事战争、采矿、科学探索、法律执行、医疗救护等一系列关系国计民生的关键领域。如今它们已不仅仅是一种工具,而是人类生活的助手、工作的搭档,发挥着日益重要的作用。环境问题已经成为当今世界一个全球化问题,然而环境是否被污染、污染程度如何,都需要由实验数据来评定,这就需要进行现场观测。在大范围恶劣环境中,第二代机器人无法工作,监测船也难以到达,此时就需要有一种智能探测机器人。
本文正是基于上述需求,研究了近海智能探测机器人的软硬件实现及导航算法。近海智能探测机器人能够实现自主避障、自主导航、错误报警和自救等功能,同时工作人员拥有机器人优先控制权,可以通过工作站发送指令控制机器人,以便在机器人无法做出判断或遇到意外情况时给予指示,更好地完成全方位探测。
近海智能探测机器人系统是一个软硬件相结合的平台。硬件系统包括:中央控制系统、监测系统、导航系统、避障系统、视觉系统、动力系统、电力系统和通讯系统。声纳在本系统中发挥重要作用,一方面,通过声纳可以探测深度信息,进而实现海底地形重建;另一方面,声纳可以识别障碍物,协助实现机器人自主避障。视觉传感器将捕获数据传输到客户端控制系统(即机器人中心控制系统)进行处理,然后压缩发送到工作站,在工作站可实现海底视觉地图重构。本文分模块对硬件系统进行了详细介绍,并给出部分电路的电路图。软件系统包括:操作系统、驱动程序和应用软件,操作系统是底层软件,本系统中采用LINUX操作系统,通过对LINUX内核剪裁,编译适合本系统的内核;驱动程序是软硬件之间的桥梁,应用程序通过驱动程序才能访问硬件设备;应用软件包括服务器端控制系统和客户端系统,客户端系统包括中央控制系统和各子系统应用软件。
智能机器人一个重要特征就是能实现自主导航,导航技术是移动机器人实现完全自主的关键环节。根据环境信息的完整程度、导航指示信号类型、导航地域等因素不同,可将移动机器人的导航方式分为航迹推算导航、惯性导航、地图导航、主动信标导航、路标导航、GPS导航、视觉导航和其它方式导航。本系统采用GPS/DR(航位推算)组合导航,由于GPS精度限制,近海智能探测机器人定位精度只能达到十到二十米,为提高GPS定位精度,本系统将航位推算与GPS导航相结合。同时本文研究了UPF(无迹粒子滤波)算法,并对算法进行改进后运用于GPS/DR导航,在实验结果中证明了算法有效。
本论文分五大部分对系统的软硬件实现及导航算法进行了介绍。第一部分介绍了本课题的研究背景、国内外研究现状以及本课题的实际应用价值,第二部分介绍了近海智能探测机器人的硬件架构并详细介绍了各个子系统的硬件选型及电路实现。第三部分介绍了系统的软件架构,分三个层次介绍了软件系统的实现。第四部分介绍了系统的导航算法,并在实验结果中验证了算法有效性。第五部分对论文内容进行了总结,并根据目前相关技术的发展趋势、研究现状做出展望。
作者参与了近海智能探测机器人硬件选型、电路图绘制、PCB板绘制等工作;参与了嵌入式操作系统内核升级、驱动程序开发、串口应用程序设计等工作;研究了无迹粒子滤波,并对算法复杂度进行了改进,应用于GPS/DR的组合导航中,在实验结果中验证了其有效性。
Intelligent Robots are, or soon will be, used in such critical domains as search and rescue, military battle, mine and bomb detection, scientific exploration, law enforcement, and hospital care. They are more than a tool, but assistants in our life and partners in our work. They are playing a more and more important role in our daily life. Environment becomes a global issue now. Experiment data is used to tell whether an environment is polluted or what the degree of the pollution is. The data is easy to get in normal environment such as farm. However, it’s really a hard work to get data in abominable environment in a large scope. The second generation robot is not able to work in such environment, nor is a monitoring ship. That’s why the third generation monitoring robot comes out.
The system introduced in this paper is based on the requirement, which is introduced in the previous paragraph. This paper introduces the hardware and software of the intelligent inshore monitoring robot -“Off-Shore”as well as the navigation system.“Off-Shore”implements the functionality of Self Collision preventing, Self Navigation, Fault and Alarm, Self Rescue etc.. However, the operators in the workstation have higher priority over the control the robot, so that the system could handle emergency.
Hardware of“Off-Shore”includes Central Control System, Monitoring System, Navigation System, Collision-Preventing System, Imaging System, Driving System, Power System and Telecommunication System. Sonar is used to get the depth information of the district we are monitoring. The Vision System will send back image information around the robot, so that a map of the monitoring district could be created. A detailed introduction of the hardware is given in the second part of this paper.
Software of“Off-Shore”covers Operating System, Drivers and Applications. Operating System for the robot is Linux. Linux OS could be deployed according to requirement. Drivers are a bridge, which bring the software and hardware together. Applications include server applications and client applications, while client applications cover every subsystem application. A detailed introduction of software is introduced in the third part of this paper.
A base for intelligent robot is self navigation. Navigation is very important in robot design. There are many navigation systems, such as DR (DEAD RECKONING), INS (Inertial Navigation System), GPS, Signpost Navigation, SLAM, Vision-Based Navigation System, Taste-Based Navigation System etc.. The navigation system of“Off-Shore”is a combined navigation system GPS/DR base on improved UPF (Unscented Particle Filter) algorithm. Detailed information about the navigation system is introduced in the fourth part of this paper. The results of experiment for this algorithm are also shown in this section.
The author of this paper takes part in selection of equipment, design of hardware, upgrade of Linux kernel, development of some drivers and coding for serial applications. UPF is introduced in this paper. An improved UPF is brought up, and the algorithm is used in“Off-Shore”navigation system.
本文正是基于上述需求,研究了近海智能探测机器人的软硬件实现及导航算法。近海智能探测机器人能够实现自主避障、自主导航、错误报警和自救等功能,同时工作人员拥有机器人优先控制权,可以通过工作站发送指令控制机器人,以便在机器人无法做出判断或遇到意外情况时给予指示,更好地完成全方位探测。
近海智能探测机器人系统是一个软硬件相结合的平台。硬件系统包括:中央控制系统、监测系统、导航系统、避障系统、视觉系统、动力系统、电力系统和通讯系统。声纳在本系统中发挥重要作用,一方面,通过声纳可以探测深度信息,进而实现海底地形重建;另一方面,声纳可以识别障碍物,协助实现机器人自主避障。视觉传感器将捕获数据传输到客户端控制系统(即机器人中心控制系统)进行处理,然后压缩发送到工作站,在工作站可实现海底视觉地图重构。本文分模块对硬件系统进行了详细介绍,并给出部分电路的电路图。软件系统包括:操作系统、驱动程序和应用软件,操作系统是底层软件,本系统中采用LINUX操作系统,通过对LINUX内核剪裁,编译适合本系统的内核;驱动程序是软硬件之间的桥梁,应用程序通过驱动程序才能访问硬件设备;应用软件包括服务器端控制系统和客户端系统,客户端系统包括中央控制系统和各子系统应用软件。
智能机器人一个重要特征就是能实现自主导航,导航技术是移动机器人实现完全自主的关键环节。根据环境信息的完整程度、导航指示信号类型、导航地域等因素不同,可将移动机器人的导航方式分为航迹推算导航、惯性导航、地图导航、主动信标导航、路标导航、GPS导航、视觉导航和其它方式导航。本系统采用GPS/DR(航位推算)组合导航,由于GPS精度限制,近海智能探测机器人定位精度只能达到十到二十米,为提高GPS定位精度,本系统将航位推算与GPS导航相结合。同时本文研究了UPF(无迹粒子滤波)算法,并对算法进行改进后运用于GPS/DR导航,在实验结果中证明了算法有效。
本论文分五大部分对系统的软硬件实现及导航算法进行了介绍。第一部分介绍了本课题的研究背景、国内外研究现状以及本课题的实际应用价值,第二部分介绍了近海智能探测机器人的硬件架构并详细介绍了各个子系统的硬件选型及电路实现。第三部分介绍了系统的软件架构,分三个层次介绍了软件系统的实现。第四部分介绍了系统的导航算法,并在实验结果中验证了算法有效性。第五部分对论文内容进行了总结,并根据目前相关技术的发展趋势、研究现状做出展望。
作者参与了近海智能探测机器人硬件选型、电路图绘制、PCB板绘制等工作;参与了嵌入式操作系统内核升级、驱动程序开发、串口应用程序设计等工作;研究了无迹粒子滤波,并对算法复杂度进行了改进,应用于GPS/DR的组合导航中,在实验结果中验证了其有效性。
Intelligent Robots are, or soon will be, used in such critical domains as search and rescue, military battle, mine and bomb detection, scientific exploration, law enforcement, and hospital care. They are more than a tool, but assistants in our life and partners in our work. They are playing a more and more important role in our daily life. Environment becomes a global issue now. Experiment data is used to tell whether an environment is polluted or what the degree of the pollution is. The data is easy to get in normal environment such as farm. However, it’s really a hard work to get data in abominable environment in a large scope. The second generation robot is not able to work in such environment, nor is a monitoring ship. That’s why the third generation monitoring robot comes out.
The system introduced in this paper is based on the requirement, which is introduced in the previous paragraph. This paper introduces the hardware and software of the intelligent inshore monitoring robot -“Off-Shore”as well as the navigation system.“Off-Shore”implements the functionality of Self Collision preventing, Self Navigation, Fault and Alarm, Self Rescue etc.. However, the operators in the workstation have higher priority over the control the robot, so that the system could handle emergency.
Hardware of“Off-Shore”includes Central Control System, Monitoring System, Navigation System, Collision-Preventing System, Imaging System, Driving System, Power System and Telecommunication System. Sonar is used to get the depth information of the district we are monitoring. The Vision System will send back image information around the robot, so that a map of the monitoring district could be created. A detailed introduction of the hardware is given in the second part of this paper.
Software of“Off-Shore”covers Operating System, Drivers and Applications. Operating System for the robot is Linux. Linux OS could be deployed according to requirement. Drivers are a bridge, which bring the software and hardware together. Applications include server applications and client applications, while client applications cover every subsystem application. A detailed introduction of software is introduced in the third part of this paper.
A base for intelligent robot is self navigation. Navigation is very important in robot design. There are many navigation systems, such as DR (DEAD RECKONING), INS (Inertial Navigation System), GPS, Signpost Navigation, SLAM, Vision-Based Navigation System, Taste-Based Navigation System etc.. The navigation system of“Off-Shore”is a combined navigation system GPS/DR base on improved UPF (Unscented Particle Filter) algorithm. Detailed information about the navigation system is introduced in the fourth part of this paper. The results of experiment for this algorithm are also shown in this section.
The author of this paper takes part in selection of equipment, design of hardware, upgrade of Linux kernel, development of some drivers and coding for serial applications. UPF is introduced in this paper. An improved UPF is brought up, and the algorithm is used in“Off-Shore”navigation system.
引文
[1] http://baike.baidu.com/view/483166.htm.
[2]水面救助机器人.中国科技信息,2003.
[3]荣玉斌,胡英,马孜.基于全景视觉的海上救助机器人.清华大学学报(自然科学版),2007,CNKI:SUN:QHXB.0.2007-S2-047.
[4]朱洪前,桂卫华,唐斌,王随平.深海采矿机器人行走系统模糊控制研究.矿业研究与开发,2008,ISSN 1005-2763,CN 43-1215/T.
[5]海底_哈勃望远镜_海底探测机器人浅介。海洋科技,2004.3.
[6]中国应对气候变化国家方案.中国国家发展和改革委员会组织编制,2007年6月印.
[7]徐玉如,肖坤.智能海洋机器人技术进展.自动化学报,2007,33(5).
[8]兵器知识,2006 ,01.
[9] http://www.searobotics.com/.
[10]刘津.美海军重整无人潜航器总体规划重新定义能力.鱼雷技术,2005,13(1).
[11] Cheng, X. On-line collision-free path planning for service and assembly tasksby a two-arm robot. Robotics and Automation,1995. Proceedings., 1995 IEEE International Conference on Volume 2, Issue , 21-27 May 1995 Page(s):1523 -1528 vol.2 Digital Object Identifier 10.1109/ROBOT.1995.525491.
[12] Yuh, J. Underwater robotics. ICRA 2000: IEEE International Conference on Robotics and Automation,2000,932-937.
[13]国军工信息网http://www.jungong.info/info/detail/5-6547.html.
[14]上海交通大学海洋工程国家重点实验室开放课题基金指南.
[15]张建军.基于RS-485网络的水下机器人执行机构.水雷战与舰船防护,2007,03(011).
[16]于延凯,林扬.水下机器人耐压舱弹塑性稳定性的一种简易计算方法.机器人,2003,1002-0446(2003)01-0015-03.
[17]章艳,罗高生,王峰,顾临怡.深海压力适应型水下机器人压力补偿技术.机电工程,2007,24(04).
[18]刘军考,陈在礼,陈维山,王力刚.水下机器人新型仿鱼鳍推进器.机器人,2000, 1002-0446(2000)05-0427-06.
[19]孙立宁.机器人技术国内外发展状况.国内外机电一体化技术,2002,4.
[20]杭观荣,曹国辉,王振龙,赵万生. SMA驱动的仿生机器人研究现状及其展望.微特电机,2006,11.
[21] Shahidi R, Shayman M, Krishnaprasad P S. Mobile robot navigation using potential functions[A]. In Proc. IEEE International Conference on Robotics and Automation[C], New York, 1991: 2047-2053.
[22] Borenstein J, Koren Y. Real-time obstacle avoidance for fast mobile robots in clustered environments[A]. IEEE Transactions on Systems, Man, and Cybernetics[C], 1989, 19(5): 1179-1187.
[23] Koren Y, Borenstein J. Potential field methods and their inherent limitations for mobile robot navigation[A]. In Proc.IEEE International Conference on Robotics and Automation[C], California, 1991:1398--1404.
[24]韩世超,左韬.基于信息融合的GPS航位推算组合导航系统设计.科技创新导报,2008,1674-098X(2008)10(c)-0023-02.
[25] K.Demirli,í.B.Turksen.Sonar Based Mobile Robot Localization by Using Fuzzy Triangulation. Robotics and Autonomous Systems.2000,33(2-3):109-123.
[26] R.Carelli,E.O.Freire.Corridor Navigation and Wall-Following Stable Control for Sonar-Based MobileRobots.Robotics and Autonomous Systems.2003, 45(3-4):235-247.
[27] E.Delgado,A.Barreiro.Sonar-Based Robot Navigation Using Nonlinear Robust Observers. Automatica.2003,39(7):1195-1203.
[28]王栋耀,马旭东,戴先中.基于声纳的移动机器人沿墙导航控制.机器人.2004,26(4):347-350.
[29] A.Diosi,L.Kleeman.Advanced Sonar and Laser Ranger Finder for Simultaneous Localization and Mapping.Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendar, Japan, September 28–October 2,2004:1854-1859.
[30] V.Subramanian,T.F.Burks and A.A.Arroyo.Development of Machine Vision and Laser Radar Based Autonomous Vehicle Guidance Systems for Citrus Grove Navigation. Computers and Electronics in Agriculture.2006,53(2):130-143.
[31] R.X.Li,K.CH.Di,H.Larry,et al.Rover Localization and Landing Site Mapping Technology for the2003 Mars Exploration Rover Mission.Photogrammetric Engineering and RemoteSensing.2004,70(1):77-90.
[32]杨子俊,廖凤英,韦庆. GPS在移动机器人导航定位系统中的应用.舰船电子工程,2005,6.
[33]谢黎明,查富生,李国慧,杨建军.在未知环境中作业移动机器人的定位算法.电机与控制学报,2005,1007一449X(2005)05一0428一04.
[34]郝凯,孟正大.基于卡尔曼滤波的室内服务机器人定位.华中科技大学学报(自然科学版),2008, 1671-4512(2008)S1-0193-03.
[35]郭彤颖,蔡安勇,郑春晖.移动机器人导航与定位技术研究进展.科技广场,2008,7.
[36]孙琼.嵌入式Linux应用程序开发详解.北京:人民邮电出版社,2006. 111~116.
[37] YF-S3C2410-III使用手册.深圳:深圳远峰计算机技术有限公司,2005. 2~29.
[38]钱德俊,张哲,胡晨. NMEA0183协议解析.电子器件,2007, 1005-9490(2007)02-0698-04.
[39]马闯,殷波,马文帅.水上机器人三维实时避障算法研究.微计算机信息,2009.5.
[40] Elfes A. Sonar-based Real-World Mapping and Navigation. IEEE Journal of Robotics and Automation, 1987, 3(3): 249~265.
[41] http://www.physorg.com/news6278.html.
[42]孙天泽,袁文菊.嵌入式设计及Linux驱动开发指南一基于ARM处理器.北京:电子工业出版社,2005. 28~35,115~125.
[43] YF-S3C2410-III开发手册.深圳:深圳远峰计算机技术有限公司,2005. 2~26.
[44] http://oldfield.wattle.id.au/luv/boot.html.
[45] Linux 2.4 to 2.6 Transition Guide (IBM).
[46] http://mxhaard.free.fr.
[47]杨勇,蔡振家,周海山. Linux环境下实现串口通信.微型电脑应用,2002,18(6):36~39.
[48]陈容,吴贵清,郑善贤. Linux嵌入式设备与PC机串口通信的设计.单片机与嵌入式系统,2003,(9):72~73.
[49] http://www.jpeg.org/.
[50]蔡锐丹,许少云,甘义成. GPRS无线数据传输系统的设计与应用.电子质量,2004,(1):19~21.
[51]赵雪峰,管建和.基于PPP协议软件实现.微计算机技术,2005,(20):48~50.
[52]刘毅敏,杨君.嵌入式系统拨号接入Internet的设计与实现.微机算计信息,2005,(5):82~83.
[53]曹宁,冯忠义,沙济彰.基于客户/服务器模式的Socket网络编程.计算机工程,1999,25(2):72~74.
[54] W. Richard Stevens. UNIX Network Programming Volume 1, Third Edition the Sockets Networking API. Addison Wesley,2003:1~225.
[55]赵梅,张三同,朱刚.改进粒子滤波算法在组合导航中的应用.中国公路学报,2007 1001-7372(2007)02-0108-05.
[56]房建成,王庆,吴秋平,万德钧.改进的车载DR系统自适应扩展卡尔曼滤波模型及仿真研究.东南大学学报,1999,20(1).
[57]申立群,王宇野,冯勇,刘宏臣.模拟GPS与DR航位推算组合系统的地图匹配算法.哈尔滨师范大学自然科学学报.2004.20(5).
[58]万磊,李璐,刘建成,徐玉如.一种基于航位推算的水下机器人导航算法.中国造船.2004 1000-4882(2004)04-0077-06.
[59]房建成,申功勋,万德钧.自适应卡尔曼滤波器在陆地.车辆导航中的应用[J].北京航空航天大学学报,1999,25(2):235-239..
[60]房建成,申功勋,万德钧.一种自适应联合卡尔曼滤波器及其在车载GPS/DR组合导航系统中的应用研究[J].中国惯性技术学报,1998,6(8):1-6.
[61] GORDON N J,SALMOND D J,SMITH A F M.No-vel Approach to Nonlinear/Non-gaussian BayesianState Estimation[J].IEEE Proceedings on Radar and Signal Processing,1993,140(2):107-113.
[62] ANDERSON B,MOORE J.Optimal Filtering[M]. Englewood Cliffs:Prentice Hall,1979.
[63]王来雄,黄士坦.一种新的粒子滤波算法.武汉大学学报(工学版),2006,39(1).
[2]水面救助机器人.中国科技信息,2003.
[3]荣玉斌,胡英,马孜.基于全景视觉的海上救助机器人.清华大学学报(自然科学版),2007,CNKI:SUN:QHXB.0.2007-S2-047.
[4]朱洪前,桂卫华,唐斌,王随平.深海采矿机器人行走系统模糊控制研究.矿业研究与开发,2008,ISSN 1005-2763,CN 43-1215/T.
[5]海底_哈勃望远镜_海底探测机器人浅介。海洋科技,2004.3.
[6]中国应对气候变化国家方案.中国国家发展和改革委员会组织编制,2007年6月印.
[7]徐玉如,肖坤.智能海洋机器人技术进展.自动化学报,2007,33(5).
[8]兵器知识,2006 ,01.
[9] http://www.searobotics.com/.
[10]刘津.美海军重整无人潜航器总体规划重新定义能力.鱼雷技术,2005,13(1).
[11] Cheng, X. On-line collision-free path planning for service and assembly tasksby a two-arm robot. Robotics and Automation,1995. Proceedings., 1995 IEEE International Conference on Volume 2, Issue , 21-27 May 1995 Page(s):1523 -1528 vol.2 Digital Object Identifier 10.1109/ROBOT.1995.525491.
[12] Yuh, J. Underwater robotics. ICRA 2000: IEEE International Conference on Robotics and Automation,2000,932-937.
[13]国军工信息网http://www.jungong.info/info/detail/5-6547.html.
[14]上海交通大学海洋工程国家重点实验室开放课题基金指南.
[15]张建军.基于RS-485网络的水下机器人执行机构.水雷战与舰船防护,2007,03(011).
[16]于延凯,林扬.水下机器人耐压舱弹塑性稳定性的一种简易计算方法.机器人,2003,1002-0446(2003)01-0015-03.
[17]章艳,罗高生,王峰,顾临怡.深海压力适应型水下机器人压力补偿技术.机电工程,2007,24(04).
[18]刘军考,陈在礼,陈维山,王力刚.水下机器人新型仿鱼鳍推进器.机器人,2000, 1002-0446(2000)05-0427-06.
[19]孙立宁.机器人技术国内外发展状况.国内外机电一体化技术,2002,4.
[20]杭观荣,曹国辉,王振龙,赵万生. SMA驱动的仿生机器人研究现状及其展望.微特电机,2006,11.
[21] Shahidi R, Shayman M, Krishnaprasad P S. Mobile robot navigation using potential functions[A]. In Proc. IEEE International Conference on Robotics and Automation[C], New York, 1991: 2047-2053.
[22] Borenstein J, Koren Y. Real-time obstacle avoidance for fast mobile robots in clustered environments[A]. IEEE Transactions on Systems, Man, and Cybernetics[C], 1989, 19(5): 1179-1187.
[23] Koren Y, Borenstein J. Potential field methods and their inherent limitations for mobile robot navigation[A]. In Proc.IEEE International Conference on Robotics and Automation[C], California, 1991:1398--1404.
[24]韩世超,左韬.基于信息融合的GPS航位推算组合导航系统设计.科技创新导报,2008,1674-098X(2008)10(c)-0023-02.
[25] K.Demirli,í.B.Turksen.Sonar Based Mobile Robot Localization by Using Fuzzy Triangulation. Robotics and Autonomous Systems.2000,33(2-3):109-123.
[26] R.Carelli,E.O.Freire.Corridor Navigation and Wall-Following Stable Control for Sonar-Based MobileRobots.Robotics and Autonomous Systems.2003, 45(3-4):235-247.
[27] E.Delgado,A.Barreiro.Sonar-Based Robot Navigation Using Nonlinear Robust Observers. Automatica.2003,39(7):1195-1203.
[28]王栋耀,马旭东,戴先中.基于声纳的移动机器人沿墙导航控制.机器人.2004,26(4):347-350.
[29] A.Diosi,L.Kleeman.Advanced Sonar and Laser Ranger Finder for Simultaneous Localization and Mapping.Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendar, Japan, September 28–October 2,2004:1854-1859.
[30] V.Subramanian,T.F.Burks and A.A.Arroyo.Development of Machine Vision and Laser Radar Based Autonomous Vehicle Guidance Systems for Citrus Grove Navigation. Computers and Electronics in Agriculture.2006,53(2):130-143.
[31] R.X.Li,K.CH.Di,H.Larry,et al.Rover Localization and Landing Site Mapping Technology for the2003 Mars Exploration Rover Mission.Photogrammetric Engineering and RemoteSensing.2004,70(1):77-90.
[32]杨子俊,廖凤英,韦庆. GPS在移动机器人导航定位系统中的应用.舰船电子工程,2005,6.
[33]谢黎明,查富生,李国慧,杨建军.在未知环境中作业移动机器人的定位算法.电机与控制学报,2005,1007一449X(2005)05一0428一04.
[34]郝凯,孟正大.基于卡尔曼滤波的室内服务机器人定位.华中科技大学学报(自然科学版),2008, 1671-4512(2008)S1-0193-03.
[35]郭彤颖,蔡安勇,郑春晖.移动机器人导航与定位技术研究进展.科技广场,2008,7.
[36]孙琼.嵌入式Linux应用程序开发详解.北京:人民邮电出版社,2006. 111~116.
[37] YF-S3C2410-III使用手册.深圳:深圳远峰计算机技术有限公司,2005. 2~29.
[38]钱德俊,张哲,胡晨. NMEA0183协议解析.电子器件,2007, 1005-9490(2007)02-0698-04.
[39]马闯,殷波,马文帅.水上机器人三维实时避障算法研究.微计算机信息,2009.5.
[40] Elfes A. Sonar-based Real-World Mapping and Navigation. IEEE Journal of Robotics and Automation, 1987, 3(3): 249~265.
[41] http://www.physorg.com/news6278.html.
[42]孙天泽,袁文菊.嵌入式设计及Linux驱动开发指南一基于ARM处理器.北京:电子工业出版社,2005. 28~35,115~125.
[43] YF-S3C2410-III开发手册.深圳:深圳远峰计算机技术有限公司,2005. 2~26.
[44] http://oldfield.wattle.id.au/luv/boot.html.
[45] Linux 2.4 to 2.6 Transition Guide (IBM).
[46] http://mxhaard.free.fr.
[47]杨勇,蔡振家,周海山. Linux环境下实现串口通信.微型电脑应用,2002,18(6):36~39.
[48]陈容,吴贵清,郑善贤. Linux嵌入式设备与PC机串口通信的设计.单片机与嵌入式系统,2003,(9):72~73.
[49] http://www.jpeg.org/.
[50]蔡锐丹,许少云,甘义成. GPRS无线数据传输系统的设计与应用.电子质量,2004,(1):19~21.
[51]赵雪峰,管建和.基于PPP协议软件实现.微计算机技术,2005,(20):48~50.
[52]刘毅敏,杨君.嵌入式系统拨号接入Internet的设计与实现.微机算计信息,2005,(5):82~83.
[53]曹宁,冯忠义,沙济彰.基于客户/服务器模式的Socket网络编程.计算机工程,1999,25(2):72~74.
[54] W. Richard Stevens. UNIX Network Programming Volume 1, Third Edition the Sockets Networking API. Addison Wesley,2003:1~225.
[55]赵梅,张三同,朱刚.改进粒子滤波算法在组合导航中的应用.中国公路学报,2007 1001-7372(2007)02-0108-05.
[56]房建成,王庆,吴秋平,万德钧.改进的车载DR系统自适应扩展卡尔曼滤波模型及仿真研究.东南大学学报,1999,20(1).
[57]申立群,王宇野,冯勇,刘宏臣.模拟GPS与DR航位推算组合系统的地图匹配算法.哈尔滨师范大学自然科学学报.2004.20(5).
[58]万磊,李璐,刘建成,徐玉如.一种基于航位推算的水下机器人导航算法.中国造船.2004 1000-4882(2004)04-0077-06.
[59]房建成,申功勋,万德钧.自适应卡尔曼滤波器在陆地.车辆导航中的应用[J].北京航空航天大学学报,1999,25(2):235-239..
[60]房建成,申功勋,万德钧.一种自适应联合卡尔曼滤波器及其在车载GPS/DR组合导航系统中的应用研究[J].中国惯性技术学报,1998,6(8):1-6.
[61] GORDON N J,SALMOND D J,SMITH A F M.No-vel Approach to Nonlinear/Non-gaussian BayesianState Estimation[J].IEEE Proceedings on Radar and Signal Processing,1993,140(2):107-113.
[62] ANDERSON B,MOORE J.Optimal Filtering[M]. Englewood Cliffs:Prentice Hall,1979.
[63]王来雄,黄士坦.一种新的粒子滤波算法.武汉大学学报(工学版),2006,39(1).