全区域覆盖自主移动机器人无信标边界生成和识别的研究
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摘要
本文以全区域覆盖自主移动机器人为研究对象，着重研究其工作区域边界生成和识别技术。作者摒弃了目前国内外一些专利和机器人产品大多采用基于信标的边界生成和识别方法，提出无信标边界自主识别解决方案。文中采用组合定位系统建立边界“电子地图”，机器人在工作过程中通过定位系统实时获得当前的位置信息，并将其与存储的边界信息进行对比，判断是否到达边界。文中应用RBF人工神经网络的方法实现移动机器人航向角的精确测量，并提出了基于环境特征的定位误差修正方案，以上策略都使得基于定位系统的边界生成和识别方案具有较高的精度。最后，作者根据全区域覆盖自主移动机器人的特点，对整个方案的硬件系统和软件系统进行了设计。本课题研究为全区域覆盖移动机器人的边界生成和识别提供了一种低成本、高精度的设计方案，具有一定的理论意义和实用价值。
In the paper, a new solution to the boundary set-up and identification of unmarked work area for mobile robot is proposed. Combined localization systems are used to establish the "digital map" of the boundary and also used to acquire the current position information of mobile robot to judge whether the boundary is reached during the course of working. According to the specialties of outdoor environment, the localization systems with combined sensors and the localization algorithm based on RBF neural network are designed. The strategy based on the environment features is also applied to eliminate the accumulated error of the localization systems. The results of simulation and experiments show that the real-time locating information is reliable to ensure the veracity of boundary identification. The research presented in the paper is valuable to the development of the region-coverage mobile robots.
In the paper, a new solution to the boundary set-up and identification of unmarked work area for mobile robot is proposed. Combined localization systems are used to establish the "digital map" of the boundary and also used to acquire the current position information of mobile robot to judge whether the boundary is reached during the course of working. According to the specialties of outdoor environment, the localization systems with combined sensors and the localization algorithm based on RBF neural network are designed. The strategy based on the environment features is also applied to eliminate the accumulated error of the localization systems. The results of simulation and experiments show that the real-time locating information is reliable to ensure the veracity of boundary identification. The research presented in the paper is valuable to the development of the region-coverage mobile robots.
引文
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40 Durrant—Whyte. Sensor Models and Multi-Sensor Integration. International Journal of Robotics Research. 1988 (7):97-113
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2 C. Hornet, G. Schmid. Path Planning and Guidance Techniques for an Autonomous Mobile Robot. Robotics and Autonomous Systems. 1995, 14(2): 199-212
3 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
4 何克忠，郭木河．智能移动机器人技术研究．机器人技术与应用．1996(2)：11-13
5 Yang X., He K., Guo M. A Prototype Towards Autonomous Navigation: The THMR-Ⅲ. Proceedings of 1998 International Conference on Advances in Vehicle Control and Safety-AVCS' 98 Amiens. France. 1998
6 张朋飞，何克忠．多功能室外智能移动机器人实验平台—THMR—V．机器人．2002，24(2)：97-101
7 Hu H., Brady M. A Parallel Processing Architecture for Sensor-based Control of Intelligent Mobile Robots. Robotics and Autonomous Systems. 1996, 17:235-257
8 马兆清，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．1996，18(6)：344-348
9 朱向阳，徐梦飞，钟秉林．基于L1距离的人工势函数构造及机器人无碰路径规划方法．机械工程学报．1996，43(3)：13-18
10 吴晓涛，孙增圻．用遗传算法进行路径规划．清华大学学报(自然科学版)．1995，35(5)：14-19
11 汤文镛，杨灿军．基于遗传算法的自动导引机器人全局路径规划．机械科学与技术．2001，20(3)：380-382
12 孙树栋．遗传算法在机器人路径规划中的应用研究．西北工业大学学报．1998，16(1)：79-83
13 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
14 李智军，吕恬生．遗传算法在自主式移动机器人局部路径规划中的应用．机械设计．2000，17(7)：26-29
15 崔茂源，田彦涛．基于模糊逻辑自主移动机器人实时滚动路径规划及控制．吉林工业大学学报(自然科学版)．1999，29(1)：58-63
16 M. Maeda. Fuzzy Drive Control of an Autonomous Mobile Robot. Fuzzy Sets and Systems.1991, 39:195-204
17 艾海舟，郝放．基于人工神经元网络的移动机器人导航研究．机器人．1995，17(1)：32—35，41
18 马明山，朱绍文．室外移动机器人定位技术研究．电工技术学报．1998，13(2)：43—46
19 Brain Yamauchi. Mobile Robots Localization in Dynamic Environments using Dead Reckoning and Evidence Grids. Proceedings of the 1996 IEEE International Conference on Robotics and Automation. 1996:1401-1406
20 张军宇，何克忠．移动机器人THMR—V组合定位系统．计算机工程与应用．2001，37(15)：68-71
21 马明山，朱绍文．室外移动机器人运动方向测量系统．电子技术应用．1997(4)：9-10
22 J. Borenstein, H.R. Everetto Mobile Robot Positioning—Sensors and Techniques. Mobile Robots. 1996, 14(4):231-249
23 Barshan B.,Durrant—Whyte, H.F. An Inertial Navigation System for a Mobile Robot. Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robotics and Systems. Yokohama, Japan, July 26-30, 1993: 2243-2248
24 Barshan B., Durrant—Whyte, H.F. Inertial Navigation Systems for Mobile Robots. IEEE Transactions on Robotics and Automation. 1995,11(3): 328-342
25 董再励，王光辉．自主移动机器人激光全局定位系统研究．机器人．2000，22(3)：207-210
26 崔茂源，董再励．移动机器人激光全局定位系统的算法研究．系统工程与电子技术．2001，23(11)：73-75
27 Fukui I. TV Image Processing System. IEEE Spectrum. 1993
28 董再励，赫颖明．一种新的基于主动路标的全方位移动机器人定位系统．中科院沈阳自动化研究所
29 魏芳，董再励．用于移动机器人的视觉全局定位系统研究．机器人．2000，23(5)：400-403
30 Rencken W.D. Concurrent Localization and Map Building for Mobile Robots Using Ultrasonic Sensors. Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robotics and Systems. Yokohama, Japan, July 26-30, 1993:2192-2197
31 Rencken W.D. Autonomous Sonar Navigation in Indoor, Unknown and Unstructured Environments. 1994 International Conference on Intelligent Robots and Systems, Munich, Germany, September 12-16, 1994:127-134
32 Kelly A. J. A Feed Forward Control Approach to the Local Navigation Problem for Autonomous Vehicles. CMU Robotics Institute Technical Report. CMU-RI-TR-94-17 1994
33 袁军，王敏．智能系统多传感器融合研究进展．控制理论与应用．1994，11(5)：513-519
34 应济，陈昆昌．多传感器集成与信息融合及其应用研究．机电工程．1997，14(6)：151-152
35 管天云，蒋静坪．机器人多传感器信息的模糊融合．机电工程．1997，14(6)：113-114
36 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
37 Hackett J.K.,Shah M. Multi-Sensor Fusion: A Perspective. Proceedings of IEEE International Conference On Robotics and Automations. 1990:1324-1330
38 Kriegman D. J. A Mobile Robot: Sensing, Planning and Locomotion. Proceedings of IEEE International Conference On Robotics and Automations. 1987:402-408
39 Durrant—Whyte. Toward a Fully Decentralized Architecture for Multi-Sensors Data Fusion. Proceedings of IEEE International Conference On Robotics and Automations. 1990:1331-1336
40 Durrant—Whyte. Sensor Models and Multi-Sensor Integration. International Journal of Robotics Research. 1988 (7):97-113
41 Mckendall R., Mintz M. Robust Fusion of Location Information.
Proceedings of IEEE International Conference On Robotics and Automations. 1988:1239-1244
42 Brooks R.A. A Robust Layered Control System for A Mobile Robot. IEEE Journal of Robotics and Automation. 1986 (2):14-23
43 肖志宏，罗志增．用于证据理论实现多信息融合的一种改进算法．机器人．2000，22(1)：7-11
44 张汝波，张国印．基于势场法的水下机器人局部路径规划研究．应用科技．1994(4)：28-34
45 郭木河，何克忠．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)．1995，35(5)：7-13
46 金妙．用于汽车导航系统的光纤陀螺．自动驾驶仪与红外技术．1999(4)：27-28
47 胡卫东．光纤陀螺发展评述．红外技术．2001，23(5)：29-33
48 Belknap R. The Information Fusion Problem and Ruled Based Hypotheses Applied to Complex Aggregations of Image Events. IEEE International Conference on Computer Vision and Pattern Recognition. 1986:227-234
49 夏熙梅．差分GPS定位技术及应用．现代情报．2002，22(3)：99-100
50 王军，于洪喜．差分GPS定位技术．空间电子技术．2001(1)：107-110
51 Wojtkowski. Automatic Lawn Mower Vehicle. US Patent, No. 5,163,273 1992
52 Shyu. Automatic WorkingVehicular System. US Patent, No. 4,919,224 1990
53 Griffin. Automatic Lawn Mower. US Patent, No. 4,133,404 1979
54 Shurman. Automatic Self-guiding Lawn Mower and Mowing Method. US Patent, No. 5,007,234 1991
55 Rafaels. Method of Guiding A Robotic Lawn Mower. US Patent, No. 4,777,785 1988
56 Chen. Robotic Lawn Mower. US Patent, No. 4,694,639 1987
57 Petersson. Electronic Bordering System. W099/15941 1999
58 Joel Garrigou. Robot Mower for Grass Round Guardrails. FR 2,695,422 1994
59 Peless. Navigation Method and System for Autonomous Machines with Markers Defining the Working Area. US Patent, No. 6,417,641 2000
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