基于信息融合和多行为决策的自主机器人导航系统
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摘要
随着现代化工业的发展，机器人在社会工业生产、国防科技、服务性行业、教育文化等各个方面的运用越来越广。自主式移动机器人作为机器人学中的一个重要分支，是一个集环境感知，动态决策与规划，行为控制与执行等多种功能于一体的综合系统，引起越来越多的专家学者和工程技术人员的研究兴趣。
对于移动机器人来说，关键智能技术是自动规划技术和基于传感器的智能，机器的智能行为包括知识理解，推测，感觉，认识，推理，归纳，推断，计划，反应，学习和问题求解等，涉及的领域包括图像理解，语言和文字符号的处理与理解，知识的表达和获取，学习，智能运动。
本论文研究的主要内容是基于速度矢量模型的多传感器信息融合以及采用专家系统作为多行为决策的自主式移动机器人系统。本文首先分析了移动机器人目前的研究现状，论述了目前常用的研究方法，阐明了移动机器人研究的意义。然后简单介绍了本系统开发中用到的一些基本理论知识和实现方法，首先确定了本系统要实现的目标，探讨了基于功能、行为两种控制结构混合的杂和结构及其控制体系框架，提出了一种基于速度矢量的多传感器信息融合算法，论述了基于模糊控制理论声纳数据处理方法，得到声纳的速度矢量模型，分析了图像处理中HSI颜色空间和SCT颜色空间的选择，设计了一种用于图像边缘分割的模板，并且通过图像处理分析出环境的精确信息，校正声纳产生的速度矢量模型，实现了声纳数据和视频数据的信息融合，为高层的行为规划提供可靠的依据。本文还介绍了一种基于地图匹配的机器人定位方法，详细论述了基于专家系统的机器人高层规划，最后简单介绍了本系统的试验情况，对本系统开发中的工作予以总结，同时展望今后的研究工作目标。
With the development of mordem industry, robots have been applied widely in all fields such as social production, defense industrys, service trade, intellectrual education and so on. Automobile robot ,which is an integrated system containing all kinds of functions by which the robot can perceive the changes of its environmentjnake plan and decision dynamically, execute predefined decision and control the robot's behaves, has been one of important research branches of robotics. Many experts and engineers from different countries are more and more intrested hi this research field.
In automobile robotics the key intellectual techonologies are the automatic plan techonique and the intelligence of sensors . The machine's intelligence includes perception of knowledges, presume, feeling, recognition, reasoning, induction, deduction, plan, response, learning and problem solution etc. The fields it covers include image perception, language and character symbol's treatment and perception and the knowledge's expression, acquisition , learning, intelligent motion.
The research of an automobile robot, which is based on multisensor' s information fusion with a velocity vector model and multibehave decision made by an expert system is the main content of this paper. At the beginning of this thesis , the development of research on automobile robot is presented. The main present research methods are discussed and the meaning of automobile robot is emphasized . Then some principles and techoniques used hi the development of this system are presented. After that we discussed three control architectures : the control architecture based on functions , the control architecture based on behaves and the hybrid control architecture and their structures. We also put forward a mutisensor's information fusion arithmetic based on velocity vector mode .La this part , A velocity vector mode based on sonar's data procession is made by fuzzy control theories.To modify this soanr's velocity vector mode and get a more reliable mode, we introduce the realtime image procession into the construction of velocity vector. For this purpose, we analysis the choice of the HSI color mode and SCT color mode and design a template for
image edge segmentation . In this way , we can obtain the reliable visual information of environment , realize the information fusion of sonar data and vision data and provide reliable reason for higher control level, that is , the behave schedule level.In this paper , a location method of robot based on map matching is dwelled on and we dwell on the robot's high level plan based on expert system.In conclusion, we give the results of experiment , meanwhile , we summarize our work and put up our goals for the futher research.
对于移动机器人来说，关键智能技术是自动规划技术和基于传感器的智能，机器的智能行为包括知识理解，推测，感觉，认识，推理，归纳，推断，计划，反应，学习和问题求解等，涉及的领域包括图像理解，语言和文字符号的处理与理解，知识的表达和获取，学习，智能运动。
本论文研究的主要内容是基于速度矢量模型的多传感器信息融合以及采用专家系统作为多行为决策的自主式移动机器人系统。本文首先分析了移动机器人目前的研究现状，论述了目前常用的研究方法，阐明了移动机器人研究的意义。然后简单介绍了本系统开发中用到的一些基本理论知识和实现方法，首先确定了本系统要实现的目标，探讨了基于功能、行为两种控制结构混合的杂和结构及其控制体系框架，提出了一种基于速度矢量的多传感器信息融合算法，论述了基于模糊控制理论声纳数据处理方法，得到声纳的速度矢量模型，分析了图像处理中HSI颜色空间和SCT颜色空间的选择，设计了一种用于图像边缘分割的模板，并且通过图像处理分析出环境的精确信息，校正声纳产生的速度矢量模型，实现了声纳数据和视频数据的信息融合，为高层的行为规划提供可靠的依据。本文还介绍了一种基于地图匹配的机器人定位方法，详细论述了基于专家系统的机器人高层规划，最后简单介绍了本系统的试验情况，对本系统开发中的工作予以总结，同时展望今后的研究工作目标。
With the development of mordem industry, robots have been applied widely in all fields such as social production, defense industrys, service trade, intellectrual education and so on. Automobile robot ,which is an integrated system containing all kinds of functions by which the robot can perceive the changes of its environmentjnake plan and decision dynamically, execute predefined decision and control the robot's behaves, has been one of important research branches of robotics. Many experts and engineers from different countries are more and more intrested hi this research field.
In automobile robotics the key intellectual techonologies are the automatic plan techonique and the intelligence of sensors . The machine's intelligence includes perception of knowledges, presume, feeling, recognition, reasoning, induction, deduction, plan, response, learning and problem solution etc. The fields it covers include image perception, language and character symbol's treatment and perception and the knowledge's expression, acquisition , learning, intelligent motion.
The research of an automobile robot, which is based on multisensor' s information fusion with a velocity vector model and multibehave decision made by an expert system is the main content of this paper. At the beginning of this thesis , the development of research on automobile robot is presented. The main present research methods are discussed and the meaning of automobile robot is emphasized . Then some principles and techoniques used hi the development of this system are presented. After that we discussed three control architectures : the control architecture based on functions , the control architecture based on behaves and the hybrid control architecture and their structures. We also put forward a mutisensor's information fusion arithmetic based on velocity vector mode .La this part , A velocity vector mode based on sonar's data procession is made by fuzzy control theories.To modify this soanr's velocity vector mode and get a more reliable mode, we introduce the realtime image procession into the construction of velocity vector. For this purpose, we analysis the choice of the HSI color mode and SCT color mode and design a template for
image edge segmentation . In this way , we can obtain the reliable visual information of environment , realize the information fusion of sonar data and vision data and provide reliable reason for higher control level, that is , the behave schedule level.In this paper , a location method of robot based on map matching is dwelled on and we dwell on the robot's high level plan based on expert system.In conclusion, we give the results of experiment , meanwhile , we summarize our work and put up our goals for the futher research.
引文
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[2]欧青立，何克忠．室外智能移动机器人的发展及其相关技术研究．机器人，2000，22(6)：519-526
[3]李贻斌，李彩虹，刘明，周风余，苏学成．移动机器人导航技术．山东矿业学院学报(自然科学版)，1999，18(3)：67-71
[4] MIGUEL SCHNEIDER-FONTAN. Plan Execution Based on Active Perception: Adding Hints to Plans. Autonomous Robots, 1999,6:53-68
[5]洪炳熔，刘新宇．基于视觉的足球机器人系统．计算机应用研究，2001，1：1-3
[6]李实，徐旭明，叶榛，孙增圻．国际足球机器人比赛及其相关技术．机器人，2000，22(5)：420-426
[7] LrChen, Dong-Yueh Liu.An Efficient Algorithm for Finding a Collision-free Path Among Polyhedral Obstacles. Journal of Robotics Systems, 1990,7(1): 129-137
[8]李枚毅，蔡自兴．改进的进化编程及其在机器人路径规划中的应用．机器人，2000，22(6)：490-494
[9]王宏，何克忠，张钹．基于收缩法与数值导航函数的机器人路径规划．清华大学学报(自然科学版)，1998，38(1)：116-119
[10] Hu H,Brady M. A Parallel Processing Architecture for Sensor-based Control of Intelligent Mobile Robots. Robotics and Autonomous Systems, 1996,17:235-257
[11] Axel GroBmaan, Riccardo Poli. Robust mobile robot localisation from sparse and noisy proximity readings using Hough transform and probability grids. Robtics and Autonomous Systems,2001(31): 1-18
[12] A. Curran, K.J.Kyriakoppulos.Ultrasonic Navigation for a Wheeled Nonholonomic Vehicle[J]. Journal of Intelligent and Robotic Systems, 1995,12
[13]张明路，焦新境，曹作良．一种新的移动机器人路径跟踪控制策略．河北工业大学学报，2000，29(1)：71-75
[14]杨欣欣，何克忠，郭木河，张鈸．基于神经网络的室外移动机器人前轮转向模型．自动化学报，2000，26(2)：154-161
[15]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
[16]袁曾任，高明．在动态环境中移动机器人导航和避碰的一种新方法．机器人，2000，22(2)：81-88
[17]O.Khatib. Real-time obstacle avoidance for manipulators and mobile robots. IEEE International Conference on Robotics and Automation, St. Louis, 1985:500-505
[18]L.McFetridge,M.Yousef Ibrahim.New Technique of Mobile Robot Navigation Using a Hybrid Adaptive Fuzzy-potemtial Field Approach. Computers ind.Engng, 1998,35(3):471-474
[19]杨明，王宏，何克忠，张钹．基于激光雷达的移动机器人环境建模与避障．清华大学学报(自然科学版)2000，40(7)：112-116
[20]A.Curran,K.J.Kyriakoppulos.Ultrasonic Navigation for a Wheeled Nonholonomic Vehicle[J]. Journal of Intelligent and Robotic Systems, 1995,12
[21]何友，王国宏，陆大金，彭应宁．多传感器信息融合及应用．电子工业出版社．2000．11
[22]王文剑．基于改进的BP网络模型的分类器的设计与实现．计算机工程与设计，1997，18(5)：43-45
[23]NOEL E.SHARKEY. Learning from Innate Behaviors:A Quantitative Evaluation of Neural Network Controllers. Autonomous Robots, 1998,5:317-334
[24]张奇，顾伟康．基于多传感器数据融合的环境理解及障碍物检测算法．机器人，1998，20(2)：104-110
[25]邬永革，黄炯，杨静宇．基于多传感器信息融合的机器人障碍物检测和环境建模．自动化学报，1997，23(5) 641-648
[26]贾云得编著．机器视觉．北京，科学出版社，2000
[27]郭木河，何克忠，陶西平，敖旭辉．室外智能机器人的局部规划与导航控制方法．清华大学学报(自然科学版)，1995，5(35)：7-12
[28]邹小兵，蔡自兴，刘娟，许永华．一种移动机器人的局部路径规划方法．中国人工智能学会第九届学术年会暨中国人工智能学会成立20周年庆祝大会论文集，2001：947-950
[29] F. MATIA, A.JIMENEZ. Multiseusor Fusion: An Autonomous Mobile Robot.Journal of Intelligent and Robotic Systems, 1998,(22): 129-141
[30] Michael D.Wagner, Dimitrios Apostolopoulos, Kimberly Shillcutt, Benjamin Shamah, Reid Simmons, William "Red"Whittaker. The Science Autonomy System of the Nomad Robot. Proceedings of the 2001 IEEE International Conference on Robotics & Automation Seoul, Korea, 2001,5: 1742-1749
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