障碍环境中的多机器人编队研究
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摘要
多机器人编队指一组机器人保持一定的队形共同运动的过程,是多机器人系统研究的重要内容,可应用于航空、航天、交通、军事等多个领域。目前学者已提出了许多典型的编队方法,包括领队-跟踪法、虚拟结构法、行为主义方法等。这些方法均能保证编队控制的收敛性,但主要解决的是系统内部的协调问题,对外部环境带来的影响,没有充分考虑,因此无法直接应用于普遍存在动、静态障碍物的生活环境,大大限制了多机器人编队在服务机器人领域的应用。为了解决上述问题,我们将编队分解为轨迹和队形两个部分,通过路径规划和行为切换来实现障碍环境的安全运动和队形保持,具体做了以下工作:
1.讨论了编队控制的三层结构,自上而下包括任务层、规划层和控制层三个部分,并对基于领队跟踪法的队形保持和基于人工势场法的运动控制进行了较为详细的分析。
2.针对编队运动过程中队形保持和避障要求之间的矛盾,提出了一种静态障碍物环境下的刚性编队方法。该方法通过各个机器人传感器信息的融合、以及角速度的设计,使编队中的机器人可作为一个整体进行避障。实验表明,该方法不仅可以使系统在避障中保持指定的队形,还可以保证避障的轨迹足够光滑。
3.针对障碍环境中的动态障碍物一般具有自主性的特点,提出了一种基于有限状态机FSM(Finite State Machine)的刚性编队方法。该方法在动、静态障碍物的识别的基础上,通过编队速度的切换来实现障碍物的规避。实验表明,该方法不仅可以保证避障过程中的队形完整,还具有实现简单的特点。
4.为了消除机器人里程计定位的累积误差,针对生活环境中一般存在摄像头监控的情况,研究了基于单摄像头的视觉定位问题。通过合适的标记设计,提出了一种基于RGB分析和形态学处理的机器人中心定位方法。实验表明,该算法不仅可以达到满意的定位精度,还可应用于不同的光照场合,消除地面反光的干扰。
上述工作的有效性均得到了仿真和实验的验证,具有向更复杂环境推广的价值和潜力。
Multi-robot formation system refers to a group of robots which move together with a certain formation. It is important in research of multi-robot system, and can be applied in many fields like aviation, aerospace, transportation, military, and so on. So far, many typical formation methods have already been proposed, including leader-follower, virtual structure, behaviorism, etc. These methods can guarantee the convergence of the whole formation. However, what they mainly focus on is the inner problem, but not enough for the outer ones. Thus, they can't be used in living environment in which there exist lots of obstacles both static and dynamic. As a result, it narrows the application of multi-robot formation system in the field of service robots. So, here we divide the whole formation system into two parts - trajectory and formation shape, and realize its safety and formation maintenance in obstacle-environment through path-planning and behavior-switching. Details are as follows:
1. A 3-level scheme of the formation control is discussed, including Task Level, Planning Level and Control Level. The formation maintenance based on leader-follower method and movement-control based on artificial potential method is also analysised in details.
2. To deal with the contradiction between formation maintenance and obstacle avoidance, a method of rigid formation control in environment with static obstacles in it is propose. It regards the robots in formation as an entirety by fusing information of sensors of every individual robot and designing a particular angular velocity. And this has already been tested and verified by experiments.
3. As common dynamic obstacles exist in living environment are usually autonomous, a method of rigid formation control based on FSM (Finite State Machine) is proposed. On the basis of recognition of dynamic and static obstacles, it avoids the obstacles successfully by changing the velocity of formation. It can guarantee the integrity of the formation while avoiding the obstacles, and is easy to realize as well. This has already been tested and verified by experiments.
4. In order to clear the accumulated error taken by the robot odometer, considering cameras usually exist in obstacle environments, we study how to locate obstacles by a single camera. This thesis proposed a method of locating the robot center based on the RGB-analysis and morphology with a kind of appropriate marks. It has been tested and verified by experiments to have enough accuracy and can be used in different backgrounds, especially where there is reflective.
The validity of all mentioned above has been tested and verified by simulations and experiments, and they have the potentials and are valuable for more complicated environments.
1.讨论了编队控制的三层结构,自上而下包括任务层、规划层和控制层三个部分,并对基于领队跟踪法的队形保持和基于人工势场法的运动控制进行了较为详细的分析。
2.针对编队运动过程中队形保持和避障要求之间的矛盾,提出了一种静态障碍物环境下的刚性编队方法。该方法通过各个机器人传感器信息的融合、以及角速度的设计,使编队中的机器人可作为一个整体进行避障。实验表明,该方法不仅可以使系统在避障中保持指定的队形,还可以保证避障的轨迹足够光滑。
3.针对障碍环境中的动态障碍物一般具有自主性的特点,提出了一种基于有限状态机FSM(Finite State Machine)的刚性编队方法。该方法在动、静态障碍物的识别的基础上,通过编队速度的切换来实现障碍物的规避。实验表明,该方法不仅可以保证避障过程中的队形完整,还具有实现简单的特点。
4.为了消除机器人里程计定位的累积误差,针对生活环境中一般存在摄像头监控的情况,研究了基于单摄像头的视觉定位问题。通过合适的标记设计,提出了一种基于RGB分析和形态学处理的机器人中心定位方法。实验表明,该算法不仅可以达到满意的定位精度,还可应用于不同的光照场合,消除地面反光的干扰。
上述工作的有效性均得到了仿真和实验的验证,具有向更复杂环境推广的价值和潜力。
Multi-robot formation system refers to a group of robots which move together with a certain formation. It is important in research of multi-robot system, and can be applied in many fields like aviation, aerospace, transportation, military, and so on. So far, many typical formation methods have already been proposed, including leader-follower, virtual structure, behaviorism, etc. These methods can guarantee the convergence of the whole formation. However, what they mainly focus on is the inner problem, but not enough for the outer ones. Thus, they can't be used in living environment in which there exist lots of obstacles both static and dynamic. As a result, it narrows the application of multi-robot formation system in the field of service robots. So, here we divide the whole formation system into two parts - trajectory and formation shape, and realize its safety and formation maintenance in obstacle-environment through path-planning and behavior-switching. Details are as follows:
1. A 3-level scheme of the formation control is discussed, including Task Level, Planning Level and Control Level. The formation maintenance based on leader-follower method and movement-control based on artificial potential method is also analysised in details.
2. To deal with the contradiction between formation maintenance and obstacle avoidance, a method of rigid formation control in environment with static obstacles in it is propose. It regards the robots in formation as an entirety by fusing information of sensors of every individual robot and designing a particular angular velocity. And this has already been tested and verified by experiments.
3. As common dynamic obstacles exist in living environment are usually autonomous, a method of rigid formation control based on FSM (Finite State Machine) is proposed. On the basis of recognition of dynamic and static obstacles, it avoids the obstacles successfully by changing the velocity of formation. It can guarantee the integrity of the formation while avoiding the obstacles, and is easy to realize as well. This has already been tested and verified by experiments.
4. In order to clear the accumulated error taken by the robot odometer, considering cameras usually exist in obstacle environments, we study how to locate obstacles by a single camera. This thesis proposed a method of locating the robot center based on the RGB-analysis and morphology with a kind of appropriate marks. It has been tested and verified by experiments to have enough accuracy and can be used in different backgrounds, especially where there is reflective.
The validity of all mentioned above has been tested and verified by simulations and experiments, and they have the potentials and are valuable for more complicated environments.
引文
[1]陈阅增.普通生物学,北京:高等教育出版社,1997
[2]刘华清,多机器人系统的研究内容与发展趋势,西安:电子科技大学出版社,1995
[3]崔荣鑫,徐德民,沈猛等.基于行为的机器人编队控制研究.计算机仿真,2006,2(2):137-139,226
[4]原魁,李园,房立新.多移动机器人系统研究发展近况.自动化学报,2007,8(8):785-794
[5]Cao Y U,Fukunaga A S,Kahng A B,et al.Cooperative mobile robotics:antecedents and directions.In:Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems 95.'Human Robot Interaction and Cooperative Robots',1995,1.226-234
[6]Koo T J,Shahruz S M.Formation of a group of unmanned aerial vehicles (UAVs).In:Proceedings of the American Control Conference,2001
[7]Kowalczyk W.Target assignment strategy for scattered robots building formation.In:Proceedings of the Third International Workshop on Robot Motion and Control,2002
[8]焦立男,唐振民.多机器人编队技术.高技术通讯,2007,3(3):325-330
[9]Balch T and Hybinette M.Social potentials for scalable multirobot formations.In:Proceedings of IEEE International Conference on Robotics and Automation,2000.73-80
[10]Mataric MJ.Interaction and intelligent behavior:[Technical Report].Massachusetts Institute of Technology,AITR21495,1994
[11]Desai J P.Modeling multiple teams of mobile robots:a graph theoretic approach.In:Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems,2001
[12]Tan K,Lewis M A.Virtual structures for high2precision cooperative mobile robotic control.In:Proceedings of Conference on Intelligent Robots and Systems,1996.132-139
[13]王醒策,张汝波,顾国昌.基于强化学习的多机器人编队方法研究.计算机工程,2002,28(6):15-16
[14]Mataric MJ.Reward functions for accelerated learning.In:Proceedings of the Eleventh International Conference on Machine Learning,1994.181-189
[15]Goldberg D,Mataric M J.Maximizing reward in a non-stationary mobile robot environment.Invited submission to the Best of Agents22000,special issue of Autonomous Agents and Multi-Agent Systems,2003,6(3):67-83
[16]Das A K,Fierro R,Kumar V,et al.A framework for vision based formation control.IEEE Transactions on Robotics and Automation,2002,18(5):813-825
[17]Cervera E,Martinet P,Renaud P.Towards a reliable vision based mobile robot formation control.In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems,Sendai(Japan),2004.3176-3181
[18]Naffin D J,Sukhatme G S.Negotiated formations.In:Proceedings of International Conference on Intelligent Autonomous Systems(IAS),Mar 2004.181-190
[19]郑向阳,熊蓉,顾大强.移动机器人导航和定位技术.《机电工程》,2003,20(5):35-37
[20]Gary Bradski,Adrian Kaehler著;于仕岂,刘瑞祯译.学习openCV.北京:清华大学出版社,2009.10
[21]鲍庆勇,李舜酩,沈峘,门秀花.自主移动机器人局部路径规划综述.传感器与微系统,2009,28(9):1-4,11
[22]Kirkpatrick S,Gelatt Jr C D,Vecchi P.Optimization by simulated annealing[J].Science,1983(220):671-680
[23]百度百科.模拟退火算法[2007-02-02]:http://baike.baidu.com/view/18185.htm?fr=ala0_1
[24]闫利军,李宗斌,卫军胡.模拟退火算法的一种参数设定方法研究[J].系统仿真学报,2008,20(1):245-247
[25]Oussama Khatib.Real-Time Obstacle Avoidance for Manipulators and Mobile Robots.The International Journal of Robotics Research,1986,5(1):90-98
[26]Lee S,Adams Teresa M.A fuzzy navigation system for mobile construction robot[J].Automation in Construction,1997(6):97-- 107.
[27]H Maaref,C Barret.Sensor--based fuzzy navigation of an autonomous mobile robot in an indoor environment[J].Control Engineering Practice,2000(8):757-768.
[28]Meng Wang,Liu,J.N.K.Fuzzy logic based robot path planning in unknown environment[C].Proceeding of IEEE MLC,2005:813--818.
[29]Yang S,Meng M.Real-time collision-free path planning of robot manipulators using neural network approaches[J].Autonomous Robots,2000,9(1):27-39.
[30]百度百科.蚁群算法[2009-12-26]:http://baike.baidu.com/view/539346.htm?fr=ala0_1
[31]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by a colony of cooperating agents.IEEE Transactions on SMC,Part B,1996,26(1):29-41
[32]朱庆保.复杂环境下的机器人路径规划蚂蚁算法[J].自动化学报,2006,32(4):586-593.
[33]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法[J].机器人,2005,7(2):132-136.
[34]Nilsson N J.Introduction to artificial intelligence principles[J].Rivista Informatica,1981,11(1):13-38.
[35]Stentz A.Optional and efficient path planning for partly known environment[C].Proceedings of the IEEE International Conference on Robotics and Automation,1994.
[36]Stentz A.The focused D~* algorithm for real time replanning[C].Proceedings of the International Joint Conference on Artificial Intelligence,1995.
[37]张纯刚.基于滚动窗口的移动机器人路径规划.系统工程与电子技术,2002,24(6):63-65,69
[38]Nelson H.C.Yung and Cang Ye.An Intelligent Mobile Vehicle Navigator Based on Fuzzy Logic and Reinforcement Learning[J].IEEE Transactions on Systems,Man,and Cybernetics,1999,29(2):314-321.
[39]Reynolds C.,Flocks,herds and schools:A distributed behavioral model.Computer Graphics,1987,21(4):25-34.
[40]Tu X.,Terzopoulos D.,Articial fishes:physics,locomotion,perception,behavior,Proceedings of SIGGRAPH 94 Conference,July,1994,43-50.
[41]Brogan D.C.,Hodgins J.K.,Group behaviors for systems with significant dynamics,Autonomous Robots,March 1997,4(1):137-153.
[42]Wang P.K.C.,Navigation strategies for multiple autonomous robots moving in formation,Journal of Robotic Systems,1991,8(2):177-195.
[43]Chen Q.,Luh J.Y.S.,Coordination and control of a group of small mobile robots.,In Proceedings of the IEEE International Conference on Robotics and Automation,1994,2315-2320.
[44]Mataric M.J.,Integration of representation into goal-driven behavior-based robots,IEEE Transactions on Robotics and Automation,June 1992,8(3):304-312.
[45]Balch T.,Behavioral Diversity in Learning Robot Teams,Georgia Institute of Technology,Phd thesis,1998.
[46]Parker L.E.,Designing control laws for cooperative agent teams.Proceedings of the IEEE Robotics and Automation Conference,1993,582-587.
[47]Balch T.,Arkin R.C.,Behavior-based formation control for multirobot teams,IEEE Transactions on Robotics and Automation,Dec.1998,14:926-939.
[48]Werger.B.B.,Cooperation Without Deliberation:A minimal behavior-based approach to multi-robot teams.Artificial Intelligence,1999,110:293-320.
[49]AmigoBot User's Guide,2003:3-4.
[50]何斌,马天予,王运坚,朱红莲.《Visual C++数字图像处理》.北京:人民邮电出版社.2002.4
[51]徐嵩,何衍,孙秀霞,王聪颖.一种不同高度目标的视觉定位新算法.仪器仪表学报.[已录用]
[2]刘华清,多机器人系统的研究内容与发展趋势,西安:电子科技大学出版社,1995
[3]崔荣鑫,徐德民,沈猛等.基于行为的机器人编队控制研究.计算机仿真,2006,2(2):137-139,226
[4]原魁,李园,房立新.多移动机器人系统研究发展近况.自动化学报,2007,8(8):785-794
[5]Cao Y U,Fukunaga A S,Kahng A B,et al.Cooperative mobile robotics:antecedents and directions.In:Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems 95.'Human Robot Interaction and Cooperative Robots',1995,1.226-234
[6]Koo T J,Shahruz S M.Formation of a group of unmanned aerial vehicles (UAVs).In:Proceedings of the American Control Conference,2001
[7]Kowalczyk W.Target assignment strategy for scattered robots building formation.In:Proceedings of the Third International Workshop on Robot Motion and Control,2002
[8]焦立男,唐振民.多机器人编队技术.高技术通讯,2007,3(3):325-330
[9]Balch T and Hybinette M.Social potentials for scalable multirobot formations.In:Proceedings of IEEE International Conference on Robotics and Automation,2000.73-80
[10]Mataric MJ.Interaction and intelligent behavior:[Technical Report].Massachusetts Institute of Technology,AITR21495,1994
[11]Desai J P.Modeling multiple teams of mobile robots:a graph theoretic approach.In:Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems,2001
[12]Tan K,Lewis M A.Virtual structures for high2precision cooperative mobile robotic control.In:Proceedings of Conference on Intelligent Robots and Systems,1996.132-139
[13]王醒策,张汝波,顾国昌.基于强化学习的多机器人编队方法研究.计算机工程,2002,28(6):15-16
[14]Mataric MJ.Reward functions for accelerated learning.In:Proceedings of the Eleventh International Conference on Machine Learning,1994.181-189
[15]Goldberg D,Mataric M J.Maximizing reward in a non-stationary mobile robot environment.Invited submission to the Best of Agents22000,special issue of Autonomous Agents and Multi-Agent Systems,2003,6(3):67-83
[16]Das A K,Fierro R,Kumar V,et al.A framework for vision based formation control.IEEE Transactions on Robotics and Automation,2002,18(5):813-825
[17]Cervera E,Martinet P,Renaud P.Towards a reliable vision based mobile robot formation control.In:Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems,Sendai(Japan),2004.3176-3181
[18]Naffin D J,Sukhatme G S.Negotiated formations.In:Proceedings of International Conference on Intelligent Autonomous Systems(IAS),Mar 2004.181-190
[19]郑向阳,熊蓉,顾大强.移动机器人导航和定位技术.《机电工程》,2003,20(5):35-37
[20]Gary Bradski,Adrian Kaehler著;于仕岂,刘瑞祯译.学习openCV.北京:清华大学出版社,2009.10
[21]鲍庆勇,李舜酩,沈峘,门秀花.自主移动机器人局部路径规划综述.传感器与微系统,2009,28(9):1-4,11
[22]Kirkpatrick S,Gelatt Jr C D,Vecchi P.Optimization by simulated annealing[J].Science,1983(220):671-680
[23]百度百科.模拟退火算法[2007-02-02]:http://baike.baidu.com/view/18185.htm?fr=ala0_1
[24]闫利军,李宗斌,卫军胡.模拟退火算法的一种参数设定方法研究[J].系统仿真学报,2008,20(1):245-247
[25]Oussama Khatib.Real-Time Obstacle Avoidance for Manipulators and Mobile Robots.The International Journal of Robotics Research,1986,5(1):90-98
[26]Lee S,Adams Teresa M.A fuzzy navigation system for mobile construction robot[J].Automation in Construction,1997(6):97-- 107.
[27]H Maaref,C Barret.Sensor--based fuzzy navigation of an autonomous mobile robot in an indoor environment[J].Control Engineering Practice,2000(8):757-768.
[28]Meng Wang,Liu,J.N.K.Fuzzy logic based robot path planning in unknown environment[C].Proceeding of IEEE MLC,2005:813--818.
[29]Yang S,Meng M.Real-time collision-free path planning of robot manipulators using neural network approaches[J].Autonomous Robots,2000,9(1):27-39.
[30]百度百科.蚁群算法[2009-12-26]:http://baike.baidu.com/view/539346.htm?fr=ala0_1
[31]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by a colony of cooperating agents.IEEE Transactions on SMC,Part B,1996,26(1):29-41
[32]朱庆保.复杂环境下的机器人路径规划蚂蚁算法[J].自动化学报,2006,32(4):586-593.
[33]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法[J].机器人,2005,7(2):132-136.
[34]Nilsson N J.Introduction to artificial intelligence principles[J].Rivista Informatica,1981,11(1):13-38.
[35]Stentz A.Optional and efficient path planning for partly known environment[C].Proceedings of the IEEE International Conference on Robotics and Automation,1994.
[36]Stentz A.The focused D~* algorithm for real time replanning[C].Proceedings of the International Joint Conference on Artificial Intelligence,1995.
[37]张纯刚.基于滚动窗口的移动机器人路径规划.系统工程与电子技术,2002,24(6):63-65,69
[38]Nelson H.C.Yung and Cang Ye.An Intelligent Mobile Vehicle Navigator Based on Fuzzy Logic and Reinforcement Learning[J].IEEE Transactions on Systems,Man,and Cybernetics,1999,29(2):314-321.
[39]Reynolds C.,Flocks,herds and schools:A distributed behavioral model.Computer Graphics,1987,21(4):25-34.
[40]Tu X.,Terzopoulos D.,Articial fishes:physics,locomotion,perception,behavior,Proceedings of SIGGRAPH 94 Conference,July,1994,43-50.
[41]Brogan D.C.,Hodgins J.K.,Group behaviors for systems with significant dynamics,Autonomous Robots,March 1997,4(1):137-153.
[42]Wang P.K.C.,Navigation strategies for multiple autonomous robots moving in formation,Journal of Robotic Systems,1991,8(2):177-195.
[43]Chen Q.,Luh J.Y.S.,Coordination and control of a group of small mobile robots.,In Proceedings of the IEEE International Conference on Robotics and Automation,1994,2315-2320.
[44]Mataric M.J.,Integration of representation into goal-driven behavior-based robots,IEEE Transactions on Robotics and Automation,June 1992,8(3):304-312.
[45]Balch T.,Behavioral Diversity in Learning Robot Teams,Georgia Institute of Technology,Phd thesis,1998.
[46]Parker L.E.,Designing control laws for cooperative agent teams.Proceedings of the IEEE Robotics and Automation Conference,1993,582-587.
[47]Balch T.,Arkin R.C.,Behavior-based formation control for multirobot teams,IEEE Transactions on Robotics and Automation,Dec.1998,14:926-939.
[48]Werger.B.B.,Cooperation Without Deliberation:A minimal behavior-based approach to multi-robot teams.Artificial Intelligence,1999,110:293-320.
[49]AmigoBot User's Guide,2003:3-4.
[50]何斌,马天予,王运坚,朱红莲.《Visual C++数字图像处理》.北京:人民邮电出版社.2002.4
[51]徐嵩,何衍,孙秀霞,王聪颖.一种不同高度目标的视觉定位新算法.仪器仪表学报.[已录用]