物品自动运送机器人（ACR）原型系统控制体系结构研究
|
摘要
机器人，尤其是智能机器人，作为人类自身能力的一种延伸，能够极大地提高人类的生产力和改善人类的生存环境。在过去的数十年中，各种不同类型的工业机器人已被广泛应用于各类生产制造领域，促进了制造业的蓬勃发展。近年来，随着各种基础理论和单元技术研究不断获得新的进展，(智能)机器人应用已逐步进入较制造业更为广阔的民用服务领域。与此同时，复杂的民用服务应用也对(智能)机器人的研究、设计和实现提出了更高的要求。
(智能)机器人的研究和应用对提高劳动生产率、推动科技发展具有重要意义，同时也成为衡量一个国家制造业水平和科技水平的一个重要标志。由于我国对(智能)机器人的研究起步较晚，与某些发达国家(如美国和日本)之间存在较大的差距。在此背景下，我们根据目前所具备的研究基础和实际条件，规划了一个有望在多种民用服务领域获得较广泛应用的物品自动运送机器人(ACR)原型系统研究项目。希望通过对ACR原型系统的研究，为将来的各种实际民用服务机器人系统的研究和实现打下一定的基础。
论文内容仅是ACR原型系统项目研究工作的一部分，其主要内容是关于智能机器人(IRS)控制体系结构的研究，目标是在对目前各种典型IRS控制体系结构深入研究的基础上，为ACR原型系统以及将来可能实现的各种实际ACR系统建立一个高可靠性的、灵活的、可扩展的、开放式的控制体系结构。
第一章介绍了目前国内外服务机器人与自主移动机器人的研究状况，提出了物品自动运送机器人(ACR)及其原型系统的概念，并对目前已有的典型IRS控制体系结构作了分析比较研究。
第二章在对ACR原型系统的功能目标进行具体分析和细化的基础上，给出了ACR原型系统的整体硬件结构设计方案。基于一个灵活的通信系统，为ACR原型系统建立了一个开放式的整体控制硬件架构，使得新的功能组成部分的加入变得简单易行。
第三章在对ACR原型系统的任务目标进行详细分析的基础上，参考第一章中对各种典型IRS控制体系结构的分析，为ACR原型系统建立了一个基于分层递阶思想的IRS控制体系结构理论模型，并对理论模型的各个组成部分以及所涉及到的基本概念作了较详细的说明。最后，在理论模型的基础上，建立了ACR原型系统控制体系结构的实现模型。
第四章讨论了诸如状态空间法、计划空间法、问题归约以及分层递阶规划等各种高层任务规划方法以及这些规划方法的综合集成，并使用这些方法解决了ACR原型系统的某些典型高层任务规划问题。给出了高层任务规划中的时间和资源约束问题的一种解决方案，最后描述了ACR原型系统的高层任务规划子系统的基本结构和整体规划流程。
第五章根据对ACR原型系统高层任务管理的需求特性分析，选择了过程推理系统用于具体实现决策层的高层任务管理器，并详细描述了具体实现方案。对决策层高层任务管理的实时性进行了分析，并探讨了高层任务管理中的故障和错误的恢复问题。
第六章对ACR原型系统控制体系结构任务执行层的具体特性作了详细分析，将任务执行层分解为状态/事件监视、任务执行控制以及故障诊断与恢复三部分，并分别针对这三部分的功能特性、组织结构以及运行机制等作了详细描述。同时，还为任务执行层定义了一种具体的任务描述形式化语言，用于编制任务执行层的任务过程。
第七章对ACR原型系统控制体系结构功能层的具体特性作了详细分析，为功能层建立了一个由不同功能模块组成的开放式体系结构。通过对模块内部所包含的功能/活动进行分类组织，建立了模块的功能模型。为所有功能模块的具体实现提出了一个通用框架结构，探讨了不同模块间的具体通信问题以及模块的自动/半自动生成的实现方案。
浙江大学博士学位论义 摘芙
第八章详细讨论了ACR原型系统的局部路径规划和全局路径规划问题。针对自由导引
模式，给出了一种以最小转弯次数为首要规划目标的最优局部路径规划算法，并针对此算法
提出了一种环境建模方法。在局部路径规划的基础上，提出了一种基于遗传算法的全局路径
规划算法。
第九章描述了ACR原型系统自主移动平台的几个典型实验方案及结果，并对实验结果
进行了分析。
第十章对全文进行了总结，指出了论文的不足之处，并对今后的研究工作进行展望。
As an extendibility of human capability, robot, especially intelligent robot can greatly improve human productivity and ameliorate the living circumstance. In the past decades, a lot of industry robots have been widely used in different manufacturing area and greatly accelerate the development of the manufacturing. Recently, with the progress of grounded theory and unit technology, intelligent robot application has entered into people living. These complex applications bring up higher requirements to the research, design and implementation of intelligent robots.
The research and application of intelligent robots is significant to improve productivity and to promote the development of science and technology, which becomes an important measurement of manufacture and science and technology level of a country. Because the beginning of research is late in our country, the research level of intelligent robots is much lower than that of western country. With this background, and on the basis of our practical condition we marked out an Autonomous Conveying Robot (ACR) prototype system project. We hope, through the research of ACR prototype system, we propose some basic practical information for the research and implementation of serve robots in the future.
The main topic of this dissertation is about the research of IRS control architecture, its goal is to design a high robust, adaptive, extendable and open control architecture for ACR prototype system and many future practical ACR systems.
In Chapter 1, the background of this study is introduced, and the conception of Autonomous Conveying Robot is presented, .and a concise review of some representative IRS control architecture is made.
In Chapter 2, based on the analysis of the functional object of the ACR prototype system, a whole hardware scheme of ACR prototype system is presented. Relied on a flexible communication system, an open system architecture for ACR prototype system is established. With this architecture, new component can be added easily. The detail of primary components is introduced too.
In Chapter 3, on the basis of the analysis of the task of the ACR prototype system, a IRS control architecture academic model based on hierarchical principle is presented for the ACR prototype system, and the detail of the primary conceptions and components in the model are introduced. In the end, a control architecture practical model for ACR prototype system, include its primary components, are described.
In Chapter 4, Production System is introduced at first. Then some high-level task planning method such as state space, plan space, problem reduction, hierarchical planning and the integration of these planning method are introduced, some knowledge representation methods are introduced also, and some planning method are used to solve particular task planning problem of ACR prototype system. In the end, a solution of time and resource constraints problems in the task planning are presented, and the structure of task planning subsystem and the global planning flow of the ACR prototype system is presented.
In Chapter 5, based on analysis of requirements of the high-level task supervising of the ACR prototype system, Procedural Reasoning System is selected to implement the high-level task supervisor of the decision level of the control architecture, and the detail implementation is introduced also. In the end, the real-time aspects and fault/error recover of high-level task supervisor are discussed.
In Chapter 6, based on analysis of features of the task execution level of the ACR prototype system control architecture, the task execution level is divided into situation/event surveillance, task execution control and fault/error recovery subsystems. In the end, a task description language used to code task procedure is presented.
In Chapter 7, with the analysis of features of the functional level of the ACR prototype system control architecture, an open modular architecture of functional level is presented. With the combination of the functions/activiti
(智能)机器人的研究和应用对提高劳动生产率、推动科技发展具有重要意义，同时也成为衡量一个国家制造业水平和科技水平的一个重要标志。由于我国对(智能)机器人的研究起步较晚，与某些发达国家(如美国和日本)之间存在较大的差距。在此背景下，我们根据目前所具备的研究基础和实际条件，规划了一个有望在多种民用服务领域获得较广泛应用的物品自动运送机器人(ACR)原型系统研究项目。希望通过对ACR原型系统的研究，为将来的各种实际民用服务机器人系统的研究和实现打下一定的基础。
论文内容仅是ACR原型系统项目研究工作的一部分，其主要内容是关于智能机器人(IRS)控制体系结构的研究，目标是在对目前各种典型IRS控制体系结构深入研究的基础上，为ACR原型系统以及将来可能实现的各种实际ACR系统建立一个高可靠性的、灵活的、可扩展的、开放式的控制体系结构。
第一章介绍了目前国内外服务机器人与自主移动机器人的研究状况，提出了物品自动运送机器人(ACR)及其原型系统的概念，并对目前已有的典型IRS控制体系结构作了分析比较研究。
第二章在对ACR原型系统的功能目标进行具体分析和细化的基础上，给出了ACR原型系统的整体硬件结构设计方案。基于一个灵活的通信系统，为ACR原型系统建立了一个开放式的整体控制硬件架构，使得新的功能组成部分的加入变得简单易行。
第三章在对ACR原型系统的任务目标进行详细分析的基础上，参考第一章中对各种典型IRS控制体系结构的分析，为ACR原型系统建立了一个基于分层递阶思想的IRS控制体系结构理论模型，并对理论模型的各个组成部分以及所涉及到的基本概念作了较详细的说明。最后，在理论模型的基础上，建立了ACR原型系统控制体系结构的实现模型。
第四章讨论了诸如状态空间法、计划空间法、问题归约以及分层递阶规划等各种高层任务规划方法以及这些规划方法的综合集成，并使用这些方法解决了ACR原型系统的某些典型高层任务规划问题。给出了高层任务规划中的时间和资源约束问题的一种解决方案，最后描述了ACR原型系统的高层任务规划子系统的基本结构和整体规划流程。
第五章根据对ACR原型系统高层任务管理的需求特性分析，选择了过程推理系统用于具体实现决策层的高层任务管理器，并详细描述了具体实现方案。对决策层高层任务管理的实时性进行了分析，并探讨了高层任务管理中的故障和错误的恢复问题。
第六章对ACR原型系统控制体系结构任务执行层的具体特性作了详细分析，将任务执行层分解为状态/事件监视、任务执行控制以及故障诊断与恢复三部分，并分别针对这三部分的功能特性、组织结构以及运行机制等作了详细描述。同时，还为任务执行层定义了一种具体的任务描述形式化语言，用于编制任务执行层的任务过程。
第七章对ACR原型系统控制体系结构功能层的具体特性作了详细分析，为功能层建立了一个由不同功能模块组成的开放式体系结构。通过对模块内部所包含的功能/活动进行分类组织，建立了模块的功能模型。为所有功能模块的具体实现提出了一个通用框架结构，探讨了不同模块间的具体通信问题以及模块的自动/半自动生成的实现方案。
浙江大学博士学位论义 摘芙
第八章详细讨论了ACR原型系统的局部路径规划和全局路径规划问题。针对自由导引
模式，给出了一种以最小转弯次数为首要规划目标的最优局部路径规划算法，并针对此算法
提出了一种环境建模方法。在局部路径规划的基础上，提出了一种基于遗传算法的全局路径
规划算法。
第九章描述了ACR原型系统自主移动平台的几个典型实验方案及结果，并对实验结果
进行了分析。
第十章对全文进行了总结，指出了论文的不足之处，并对今后的研究工作进行展望。
As an extendibility of human capability, robot, especially intelligent robot can greatly improve human productivity and ameliorate the living circumstance. In the past decades, a lot of industry robots have been widely used in different manufacturing area and greatly accelerate the development of the manufacturing. Recently, with the progress of grounded theory and unit technology, intelligent robot application has entered into people living. These complex applications bring up higher requirements to the research, design and implementation of intelligent robots.
The research and application of intelligent robots is significant to improve productivity and to promote the development of science and technology, which becomes an important measurement of manufacture and science and technology level of a country. Because the beginning of research is late in our country, the research level of intelligent robots is much lower than that of western country. With this background, and on the basis of our practical condition we marked out an Autonomous Conveying Robot (ACR) prototype system project. We hope, through the research of ACR prototype system, we propose some basic practical information for the research and implementation of serve robots in the future.
The main topic of this dissertation is about the research of IRS control architecture, its goal is to design a high robust, adaptive, extendable and open control architecture for ACR prototype system and many future practical ACR systems.
In Chapter 1, the background of this study is introduced, and the conception of Autonomous Conveying Robot is presented, .and a concise review of some representative IRS control architecture is made.
In Chapter 2, based on the analysis of the functional object of the ACR prototype system, a whole hardware scheme of ACR prototype system is presented. Relied on a flexible communication system, an open system architecture for ACR prototype system is established. With this architecture, new component can be added easily. The detail of primary components is introduced too.
In Chapter 3, on the basis of the analysis of the task of the ACR prototype system, a IRS control architecture academic model based on hierarchical principle is presented for the ACR prototype system, and the detail of the primary conceptions and components in the model are introduced. In the end, a control architecture practical model for ACR prototype system, include its primary components, are described.
In Chapter 4, Production System is introduced at first. Then some high-level task planning method such as state space, plan space, problem reduction, hierarchical planning and the integration of these planning method are introduced, some knowledge representation methods are introduced also, and some planning method are used to solve particular task planning problem of ACR prototype system. In the end, a solution of time and resource constraints problems in the task planning are presented, and the structure of task planning subsystem and the global planning flow of the ACR prototype system is presented.
In Chapter 5, based on analysis of requirements of the high-level task supervising of the ACR prototype system, Procedural Reasoning System is selected to implement the high-level task supervisor of the decision level of the control architecture, and the detail implementation is introduced also. In the end, the real-time aspects and fault/error recover of high-level task supervisor are discussed.
In Chapter 6, based on analysis of features of the task execution level of the ACR prototype system control architecture, the task execution level is divided into situation/event surveillance, task execution control and fault/error recovery subsystems. In the end, a task description language used to code task procedure is presented.
In Chapter 7, with the analysis of features of the functional level of the ACR prototype system control architecture, an open modular architecture of functional level is presented. With the combination of the functions/activiti
引文
[1] Meystel A. Autonomous mobile robots vehicles with cognitive control. World Scientific Publishing Co.Pte.Ltd, 1991
[2] Hart A P, Nilsson N J and Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Systems Sci and Cybern, 1968,4
[3] Lewis A R A and Bejczy A K. Planning considerations for a roving robot with arm. Proc Srd IJCAI, 1973
[4] Amosov N M, Kussul E M and V D Fomenko. Transport robot with network control system. Advanced Papers of the 4th 1JCA1, Tbilisi, 1975
[5] Dobrotin A B and Lewis R. A practical manipulator system. Proc of the 5th IJCAI, 1997
[6] Belenkov Y O, Gusev S Y, Zotov S Y, Ruzhanskiy Y I, Timofeyev A Y, Frolov Y M and Yakubovich Y A. Adaptive system for control of autonomous mobile robot. Engineer Cybernetics, no.6, 1978
[7] Moravec H P. Robot rover visual navigation. UMI Res Press, 1981
[8] Moravec H P. The Stanford car and the CMU rover. Technical Report of the Robotic Institute at Carnegie-Mellon University, Pittsburgh, 1983
[9] Moravec H P. The CMU rover. Proc of the National Conf on AI, Pittsburgh, 1982
[10] Elfes A and Talukdar S N. Distributed control system for the CMU rover. Report of the Robotic Institute at Carnegie-Mellon University, Pittsburgh, 1983
[11] Giralt G, Sobek R and Chatila R. Multi-level planning and navigation system for a mobile robot: A 1st approach to Hilare. Proc of 6th IJCAI, Tokyo, 1979
[12] Marce L, Julliere M, Place H and Perrichot H. A semi-autonomous remote controlled mobile robot. The Industrial Robot, Dec, 1980
[13] Giralt G, Chatila R and Yaisset M. An integrated navigation and motion control system for autonomous multisensory mobile robots. In Robotic Research, eds. Brady M and Paul R, MIT Press, 1984
[14] Jullire M, Le Flecher J, Marce L, Perrichot H and Place H. Detection simple d'obstacles: Bras tacticle pour robot mobile. Le Nouvel Automatisme, Apr, 1981
[15] Tachi S, Tanie K, Komoziya K, Hosoda H and Abe M. Guide dog robot-Its basic plan and some experiments with MELDOG Mark I. Mechanisms and Machine Theory, 1981,16
[16] Everett H R. A computer controlled sentry robot: A homebuilt project report. Robotics Age, Mar/Apr, 1982
[17] Everett H R. A microprocessor controlled autonomous sentry robot Master's Thesis, Naval Postgraduate Scholl, Oct, 1982
[18] Neff R. Japanese rush to robot production. Electronics, Oct, 1981
[19] Darenberg W, Panic F and Weidemann. Automated guidance system for buses and trucks. Proc of 1st Int Conf on AGYC, 1981
[20] Tajima M and Schneider F. New concepts using robot-vehicles for transport and warehousing system. 4th Int Conf on Automation in Warehousing, Tokyo, 1981
[21] The Bell and Howell family of driverless vehicles(sales pamphlet), Leeland, MI, 1982
[22] Tsugawa S, Yatabe T, Hirose T and Matsumoto S. An automobile with artificial intelligence. Proc IJCAI, 1979
[23] Linden T A, Marsh J P and Dove D L. Architecture and early experience with planning for the ALV. Proc IEEE Int Conf on Robotices and Automation, San Francisco, 1986,3
[24] Kanade T, Thorpe C and Whittaker W. Autonomous vehicle project at CMU. Proc ACM Computer Conf, Cincinnati, Feb, 1986
[25] Chavez R and Meystel A. Structure of intelligence for an autonomous vehicle. Proc of the IEEE Int Conf on Robotics and Automation, Atlanta, 1984
[26] Koch E, Yeh C, Hillel G, Meystel A and Isik C. Simulation of path planning for a system with vision and map updating. Proc of IEEE Int Conf on Robotics and Automation, St Louis, 1985
[27] Meystel A. Primer on autonomous mobility. Drexel Univ, PA, 1986
[28] Parodi A M, Nitao J J and McTamaney L S. An intelligent system for an autonomous vehicle. Proc IEEE Int Conf on Robotics and Automation, San Francisco, 1986,3
[29] Nageswara Rao S Y, Iyengar S S, Jorgensen C C and Weisbin C R. Concurrent algorithms for autonomous robot navigation in an unexplored terrain. Proc IEEE Int Conf on Robotics and Automation, San Francisco, 1986,2
[30] 田华，席裕庚，张钟俊，蒋慰孙．智能机器人系统建模新理论--环递阶模型理论．机器人， 1994，16(3) ：150-158
[31] Saridis G N. Intelligent robot control. IEEE Trans on Auto Cont. 1983,28(5) :547-557
[32] Saridis G N. Machine-Intelligent robots: A hierarchical control approach. Machine Intelligence and Knowledge Engineering for Robotic Applications, Springer-Verlag Berlin Heidelberg, German, 1987(33) : 221-234
[33] Saridis G N. Knowledge implementation: Structures of intelligent control systems. Proc of IEEE Int Sym on Intel Cont, Philadelphia, USA, Jan,1987:9-17
[34] Moed M C and Saridis G N. A boltzmann machine for the organization of intelligent machines. IEEE Trans on Sys, Man and Cyb, 1990,20(5) : 1094-1102
[35] Graham J H and Saridis G N. Linguistic decision structures for hierarchical systems. IEEE Trans on Sys , Man and Cyb, 1982,12(3) :325-333
[36] Saridis G N and Graham J H. Linguistic decision schemata for intelligent robots. Automatic, 1984,20(1) : 121-126
[37] Wang F Y and Saridis G N. A coordination theory for intelligent machines. Automatic, 1990,26(5) :833-844
[38] Saridis G N. Entropy formulation of optimal and adaptive control. IEEE Trans on Auto Cont, 1988,33(8) : 713-721
[39] Albus J S, et al. Hierarchical control for robots and teleoperators. Proc of IEEE Workshop on Intel Cont, Troy, New York, USA, Aug,1985:39-49
[40] Albus I S, et al. Hierarchical control of intelligent machines applied to space station telerobots. Proc of IEEE Int Sym on Intel Cont, Philadelphia, Pennsylvania, USA, Jan, 1987:20-26
[41] Lumia R and Albus J S. An architecture to support teleoperation and autonomy. Proc of IEEE Int Conf on Sys, Man and Cyb, Alexandria, Virginia, USA, Oct, 1987,3:1160-1164
[42] Lumia R and Albus J S. Teleoperation and autonomy for space robotics. Robotics, 1988,4:27-33
[43] Albus J S, et al. NASA/NBS standard reference model for telerobot control system architecture (NASREM), NISTTN 1235, 1989 Ed. (Supersedes NBS TN 1235 Issued July 1987) , Gaithersburg, MD, USA, Apr,1989．
[44] Brooks R A. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 1986,2(1)
[45] Brooks R A. New approach to robotics. Science, 1991,253:1227-1232
[46] Steels L and Brooks R A. The artificial life route to artificial intelligence. Lawrence Earlbaum Associates, Publishers, Hove, U.K, 1995
[47] Mataric M. Integration of representation into goal-driven, behaviour-based robots. IEEE Transaction on Robotics and Automation, 1992,8(3)
[48] 田华，蒋慰孙．智能机器人体系结构的理论研究．控制与决策，1993，8(5) ：330-337
[49]田华，席裕庚，张仲俊，蒋慰孙．智能机器人系统建模的新方法．模式识别与人工智能，1993，6(3)：261-266
[50]Barnes D P. A Behaviour Synthesis Architecture for the control of co-operate mobile robots. Advanced Robotics and Intelligent Machines, Chapter 19, Peter Peregrinus Control Engineering series, ed. Gray J O and Caldwell D
[51]Eustace D, Barnes D, Gray J O. Multiple co-operate mobile robots for unstructured environments. Proceedings of the 1993 International Conference on Advanced Robotics, Tokyo, Nov, 1993
[52]Aylett R Eustace D, Gray J O. Combining predictive and Reactive Control Strategies in Multi-agent System. IEE Press, Proc of the IEE International Conference Control, Warwick, 1994,2
[53]Neves C and Gray J O. System architectures for advanced robotics, lEE Press, Proc of the IEE International Conference Control, 1995,1
[54]冯文镛，杨灿军，陈鹰．物品自动运送机器人(ACR)原型系统总体设计．机电工程，2000,17(4)：54-57
[55]周学才，李卫平，李强．开放式机器人通用控制系统，机器人，1998，20(1)：25-31
[56]范永，谭民．机器人控制器的现状及展望．机器人，1999，21(1)：75-79
[57]饶蚺，邓岳湘，任永益．关于自动导向车最优控制的设计．控制理论与应用，1996，13(6)：838-842
[58]冯建农，柳明，吴捷．基于信息融合的模型小车ANN避障控制．控制理论与应用，1997，14(6)：847-852
[59]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
[60]黄闪，孔毅．智能机器人体系结构与与全局数据库设计与实施．沈阳工业大学学报，1999，21(1)：41-44
[61]Alami R Chatila R, Fleury S, Ghallab M and Ingrand E An architecture for autonomy. The International Journal of Robotics Research, 1998,17(4):315-337
[62]Albus J S. Outline for a theory of intelligence. IEEE Trans on Systems, Man and Cybernetics, 1991,21(3):473-509
[63]Hayes B R. A blackboard architecture for control. Artificial Intelligence, 1985:252-321
[64]Albus J S. Brains, Behavior, and Robotics. Petebourough, NH: BYTE/McGraw-Hill, 1981
[65]Miller G A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psych Rev, 1956,63:71-97
[66]Meystel A. Theoretical foundation of planning and navigation for autonomous robots, lnt J Intelligent Syst, 1987, 2:73-128
[67]Minsky M. A framework for representing knowledge. In The Psychology of Computer Vision, Winston P, Ed. New York: McGraw-Hill, 1975:211-277
[68]Saridis G N. Foundations of the theory of intelligent controls. In Proc IEEE Workshop on Intelligent Control. 1985
[69]Albus J S, McCain H G and Lumia R. NASA/NBS standard reference model for telerobot control system architecture(NASREM). Nat Inst Standards and Tech, Tech Rep 1235, Gaithersburg, MD, 1989
[70]Sacerdoti E D. A structure for plans and behavior. New York: Elsevier, 1977.
[71]Gibson J J. The ecological approach to visual perception. Ithaca, NY: Cornell Univ Press, 1966
[72]Mart D. Vision. San Francisco, CA: Freeman, 1982
[73]Aibus J S and Hong T H. Motion, depth, and image flow. In Proc IEEE Robotics and Automation, Cincinnati, OH, 1990
[74]Kent E and Albus J S. Servoed world models as interfaces between robot control systems and sensory data. Robotica, 1984,2:17-25
[75]Nilsson N J. Principles of artificial intelligence. Springer-Verlag Berlin Heidelberg, New York, 1982
[76]Nilsson N J. Artificial intelligence: A new synthesis. Morgan Kaufmann, CA, 1999
[77]Allen J, Hendler J and Tate A. Reading in planning. Morgan Kaufmann, CA, 1990
[78] Fikes R and Nilsson N. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 1971,2(3) : 189-208
[79] Fikes R, Hart P and Nilsson N. Learning and executing generalized robot plans. Artificial Intelligence, 1972,3(4) :251-288
[80] Sacerdoti E. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 1974,5(2) : 115-135
[81] Sacerdoti E. A structure for plans and behaviours. North-Holland, Amsterdam, 1977
[82] Lesser V and Erman L. A retrospective view of the Hearsay-II architecture. Proc IJCAI-77, Cambridge, MA, 1977:27-35
[83] Stefik M. Planning with constraints. Artificial Intelligence, 1981,16:111-140
[84] Tate A. Generating project networks. Proc IJCAI-77, Cambridge, MA, 1977
[85] Vere S. Planning in time: windows and durations for activities and goals. Trans Patten Anal Mach Intell, 1981,5:246-267
[86] Wilkins D E. Practical planning: Extending the classical AI planning paradigm. San Francisco: Morgan Kaufmann, 1988
[87] Chapman D. Planning for conjunctive goals. Artificial Intelligence, 1987,32(3) :333-377
[88] Currie K and Tate A. O-Plan: the open planning architecture. Artificial Intelligence, 1991,52( 1 ):49-86
[89] Georgeff M P and Lansky. Procedural knowledge. Proc of the IEEE Special Issue on Knowledge Representation, 1986,74:1383-1398
[90] Georgeff M P. Procedural control in production systems. Artificial Intelligence, 1982,18:175-201
[91] Knoblock C A. Automatically generating abstractions for planning. Artificial Intelligence, 1994,68:243-302
[92] Knoblock C A. Learning abstraction hierarchies for problem solving. Proc AAAI-90,1990:923-928
[93] Laborie P and Ghallab M. Planning with sharable resource constraints. Proc IJCAI-95, 1995:1643-1649
[94] Allen J F. An interval-based representation of temporal knowledge. Proc 7th IJCAI, Vancouver, Aug, 1981
[95] Fox M S and Smith S F. ISIS: a knowledge-based system for factory scheduling. Expert Systems, 1984,1(1 ):25-49
[96] Smith S F, Fox M S and Ow P S. Constructing and maintaining detailed production plans: investigations into the development of knowledge-based factory. AI Magazine, 1986,7(4) :45-61
[97] Sadeh N. Look-ahead techniques for micro-opportunistic job shop scheduling. PhD thesis, Carnegie Mellon Univesity, Mar, 1991
[98] Bensana E, Bel G and Dubois. OPAL: a multi-knowledge-based system for industrial job-shop scheduling. International Journal of Production Research, 1988,26(5) :795-819
[99] Erschler J, Lopez P and Thuriot C. Temporal reasoning under resource constraints: application to task scheduling. In Advances in Support Systems Research, 1990:189-194
[100] Beck H. Tosca: A novel approach to the management of job-shop scheduling constraints. In Proc 9th CIM-Europe Annual Conference, Amsterdam, 1993:138-149
[101] Allen J F. Towards a general theory of action and time. Artificial Intelligence, 1984,23:123-154
[102] Shoham Y. Temporal logics in AI: semantical and ontological considerations. Artificial Intelligence, 1987,33:89-104
[103] Ghallab M and Laruelle. Representation and control in Ixtet, a temporal planner. In Proc AIPS-94, 1994:61-67
[104] Ghallab M and Mounir A A. Managing efficiently temporal relations through indexed spanning trees.In Proc UCAI-89, 1989
[105] Ghallab M, Alami R and Chatila R. Dealing with time in planning and execution monitoring. In Robotics Research: The Fourth International Symposium, MIT Press, 1988
[106] Vilain M and Kautz H. Constraint propagation algorithms for temporal reasoning. In Proc of The Fifth National Conference on Artificial Intelligence, Aug, 1986:3 77-3 82
[107] Laffey T J et al. Real-time knowledge-based systems. AI Magazine, 1988,9(1) :27-45
[108] Hayes B R et al. Intelligent monitoring and control. Proc Int Joint Conf Artificial Intelligence, Morgan Kaufmann, San Mateo, Calif, 1989:243-249
[109] Dodhiawala R. Real-time AI systems: a definition and a architecture. Proc Int Joint Conf Artificial Intelligence, Morgan Kaufinann, San Mateo, Calif, 1989:256-261
[110] Kaelbling L P. An architecture for intelligence reactive systems. Proc 1986 Workshop on Reasoning about Actions and Plans, Morgan Kaufmann, San Mateo, Calif, 1987:395-410
[111] Georgeff M P and Ingrand F F. Decision making in an embedded reasoning system. Proc 11th Int Joint Conf Artificial Intelligence, Morgan Kaufmann, San Mateo, Calif, 1989:972-978
[112] Georgeff M P and Ingrand F F. Real-time reasoning: The monitoring and control of spacecraft systems. Proc Sixth IEEE Conf Artificial Intelligence Applications, Computer Society Press, Los Alamitos, Calif, 1990:198-205
[113] Georgeff M P and Ingrand F F. Research on procedure reasoning systems. Tech Report, SRI Int, Menlo Park, Calif, 1990
[114] Wesley L te al. Application of PRS to network management systems. Tech Report, SRI Int, Menlo Park, Calif, 1991
[115] Ingrand F F, Chatila R, Alami R and Robert F. PRS: A high level supervision and control language for autonomous mobile robots. Proc of IEEE Int Conf on Robotics and Automation, 1996:43-49
[116] Ingrand F F and Coutance V. Real-time reasoning using procedural reasoning. Tech Report, LAAS/CNRS, Toulouse, France, 1993:93-104
[117] Ingrand F F, Georgeff M P and Rao A S. An architecture for real-time reasoning and system control. IEEE Expert, 1992:34-44
[118] Firby R J. An investigation into reactive planning in complex domains. Proc Sixth National Conf Artificial Intelligence, MIT Press, Cambridge, Mass, 1987:202-206
[119] Ishida T, Sasaki Y, Nakata K and FukUhara Y. A Meta-level control architecture for production systems. IEEE Trans on Knowledge and Data Engineering, 1995,7(1) :44-52
[120] Munson J H. Robot planning, execution and monitoring in an uncertain environment. In IJCAI-73, Standford California USA, 1973:338-349
[121] Scrinivas S. Error recovery in robot system. PhD thesis, California Institute of Technology, Pasadena, California, 1977
[122] Noreils F R and Chatila. Control of mobile robot actions. In IEEE Int Conf on Robotics and Automation, 1989:701-712
[123] Noreils F R and Chatila R G A general structure for mobile robot action control. IEEE/RSJ International Workshop on Intelligent Robots and Systems, Tsukuba, Japan, 1989:550-556
[124] Noreils F R and Raja G Plan execution monitoring and control architecture for mobile robots. IEEE Trans on Robotics and Automation, 1995,11(2) :255-266
[125] Chatila R, Alami R, Degallaix B and Laruelle H. Integrated planning and execution control of autonomous robot actions. Proc Int Conf on Robotics and Automation, Nice, France, 1992:2689-2696
[126] Noreils F R. Integrating error recovery in a mobile robot control system. IEEE Int Conf Robotics and Automation, 1990:396-401
[127] Fleury S, Herrb M and Chatila R. Design of a modular architecture for autonomous robot. IEEE ICRA'94, San Diego, CA, 1994:3508-3513
[128]Camargo R F, Chatila R and Alami R. Hardware and software architecture for execution control of an autonomous mobile robot. Iht Conf on Industrial Electronics, Control, and Instrumentation, 1992:818-825
[129]Chatila R and Ferraz R. Open architecture design and inter-task/inter-module communication for an autonomous mobile robot. IEEE International Workshop on Intelligent Robots and System, 1990:717-721
[130]Hu T C, Kahng A and Robins G Optimal robust path planning in general environments. IEEE Trans Robotics Automat, 1993,9(6):775-784
[131]Brooks R A. Solving the find path problem by good representation of free space. IEEE Trans Sys, Man, Cybern, 1983,13(3):190-197
[132]Lozano-Perez T and Wesley M. An algorithm for planning collision-free paths among polyhedral obstacles. Communication ACM, 1979, 22 (10): 560-570
[133]Lozano-Perez T. Special planning: a configuration space approach. IEEE Trans Comput, 1983,32(2):108-120
[134]Mataric M J. Integration of representation into goal-driven behavior-based robots. IEEE Trans Robotics Automat, 1992,8(3): 304-312
[135]Sharir M and Schorr A. On shortest path in polyhedral spaces. SIAM J Comput, 1986,15:193-215
[136]Jerez C A, Hashimoto Y, Matsuda T and Tsuchiya T. Environment representation using enclosed obstacles and minimum-turns path planning. Advanced Robotics, 1998,12(4):351-371
[137]Hollaud J H. Adaptation in Natural and Artificial System. MIT Press, Cambridge, Mass, 1975
[138]张晓缋，戴冠中，徐乃平．一种新的优化算法——遗传算法．控制理论与应用，1995，12(3)：265—237
[139]陈根社，陈新海．遗传算法的研究与进展．信息与控制，1994，23(4)：215—222
[140]Goldberg D. Genetic Algorithms in Search, Optimization and Machine Learning. USA: Addsion-Wesley Publishing Company, 1988: 59-308
[141]马钧水，刘贵忠，贾玉兰．改进遗传算法搜索性能的大变异操作．控制理论与应用，1998，6(3)：404—407
[142]孙增圻等编著．智能控制理论与技术．清华大学出版社，1997
[143]Goldberg D E and Lingle R. Alleles, loci, and the Traveling Salesman Problem. Proceeding of an International Conference on Genetic Algorithms and Their Application. 1985:154-159
[144]Davis L. Job Shop Scheduling with Genetic Algorithms. Proceedings of an International Conference on Genetic Algorithms and Their Application. 1985: 136-140
[145]Smith D. Bin Packing with Adaptive Search. Proceedings of an International Conference on Genetic Algorithms and Their Application. 1985:202-206
[146]陈国良等．遗传算法及其应用．人民邮电出版社，1999
[147]恽为民，席裕庚．遗传算法的全局收敛性和计算效率分析．控制理论与应用，1996，13(4)：455-460
[148]孙增圻，张再兴．智能控制的理论与技术．控制与决策，1996，11(1)：1-8
[149]费敏锐，陈伯时，郎文鹏．智能控制方法的交叉综合及其应用．控制理论与应用，1996，13(3)：273-280
[150]西广成．智能控制与相对熵最小化．控制理论与应用，1999，16(1)：27-31
[151]陈建安，戴冠中，徐乃平．学习控制技术·方法和应用的发展方向．控制理论与应用，1999，16(1)：1-5
[152]刘金琨，王树青．多智能体控制系统的设计与实现．控制理论与应用，1999，16(4)：580-582
[153]叶文，杨叔子．基于过程代数的智能机器人系统建模．机器人，1998，20(1)：9-14
[154]谭群华，李伟．基于行为建模的机器人混合模糊-神经控制方法．清华大学学报，1997，37(3)：38-41
[155]张友军，朱淼良，吴春明，胡晓敏．自主式移动机器人系统的体系结构．机器人，1997，19(5)：378-383
[156]Noreils F R. Toward a robot architecture integrating cooperation between mobile robots: application to indoor environment. The International Journal of Robotics Research, 1993,12(1):79-98
[157]Wang F Y and Saridis G N. Task translation and integration specification in intelligent machines. IEEE Trans on Robotics and Automation, 1993,9(3):257-271
[158]Simmons R G Structured control for autonomous robots. IEEE Trans on Robotics and Automation, 1994,10(1):34-43
[159]Watanabe M, Onoguchi K, Kweon I and Kuno Y. Architecture of behavior-based mobile robot in dynamic environment. Proc IEEE Int Conf on Robotics and Automation, Nice, France, 1992
[160]程平，孙茂相，尹朝万，王艳红．基于分层控制的移动机器人最优运动规划．机器人，1997，19(1)：294-299
[161]宫国晖，袁曾任，贾书惠．轮式移动机器人驱动系统的动力学模型的研究．机器人，1997，19(2)：110-115
[162]周捷，翟羽健．基于神经网络的自主车辆导航路径计算．机器人，1999，21(5)：363-368
[163]李强，林良明，颜国正．一种新的移动机器人环境模型．机器人，1999，21(5)：379-385
[164]王军，黄心汉．基于神经网络进化学习的两轮小车避障控制．机器人，1996，18(5)：392-397
[165]张汝波，周宁，顾国昌，张国印．基于强化学习的智能机器人避碰方法研究．机器人，1999，21(3)：204-209
[166]齐国光，郭木河，马少平，归柽华．轮式机器人最优控制的研究．机器人，1996，18(1)：21-27，
[167]孟庆浩，彭商贤，刘大维．基于Q-M图启发式搜索的移动机器人全局路径规划．机器人，1998，20(4)：273-279
[168]梁泉，许晓鸣，张钟俊．多智能体系统协作及控制机理和基本问题分析．控制与决策，1996，11(5)：521-526
[169]梁泉，许晓鸣，何星，张钟俊．多智能体系统智能体协作层的设计与实现．上海交通大学学报，1996，30(4)：59-64
[170]席裕庚，柴天佑，恽为民．遗传算法综述．控制理论与应用，1996，13(6)：697-708
[171]孙艳丰，王众托．遗传算法在优化问题中的应用研究进展．控制与决策，1996，11(4)：425-431
[172]史奎凡，陈月辉．提高遗传算法收敛速度的方法．信息与控制，1998，27(4)：289-293
[173]郑立辉，郭亚军，潘德惠．一种基于梯度搜索的全局优化新算法．控制理论与应用，1997，14(3)：343-347
[174]吴志远，邵惠鹤，吴新余．新的进化过程遗传算法．上海交通大学学报，1997，31(12)：66-68
[175]张晓馈，戴冠中，徐乃平．遗传算法种群多样性分析研究．控制理论与应用，1998，15(12)：17-22
[176]马钧水，刘贵忠，贾玉兰．改进遗传算法搜索性能的大变异操作．控制理论与应用，1998，15(3)：404-407
[177]李书全，赵良英，史智兴，寇纪淞，李敏强．一种防止遗传算法成熟前收敛的有效算法．系统工程理论与实践，1999(5)：72-77
[178]邰宜斌，席裕庚，李秀明．一种机器人路径规划的新方法．上海交通大学学报，1996，30(4)：94-100
[179]张纪会，徐心和．一种新的进化算法——蚁群算法，系统工程理论与实践，1999(3)：84-87
[180]栾尚敏，马绍汉．搜索问题的代数描述及其算法．计算机研究与发展，1997，34(11)：801-806
[181]张德富，顾卫刚．沈平．一种旅行商问题的并行模拟退火算法．计算机研究与发展，1995，32(2)：1-4
[182]姜大立，杨西龙．杜文，周贤伟．车辆路径问题的遗传算法研究．系统工程理论与实践，1999(6)：40-45
[183]Sekhavat S, Svestka P, Laumond J P and Overmars M H. Multilevel path planning for nonholonomic robots using semiholonomic subsystems. The International Journal of Robotics Research, 1998,17(8):840-857
[184]刘书家，赵传强，李朴．虚拟86模式下实时多任务操作系统的研究与实践．研究与发展，1997，34(3)：206-211
[185]徐宝文，李东旭．设计结构图——大型实时软件设计的有效工具．计算机研究与发展，1996，33(4)：256-262
[186]肖兵，沈理．实时控制系统的面向对象软件结构与实现方法．计算机研究与发展，1995，32(8)：49-54
[187]Chochon H. Object-oriented design of mobile robot control system. In 2nd ISER Toulouse, France, Jun, 1991
[2] Hart A P, Nilsson N J and Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Systems Sci and Cybern, 1968,4
[3] Lewis A R A and Bejczy A K. Planning considerations for a roving robot with arm. Proc Srd IJCAI, 1973
[4] Amosov N M, Kussul E M and V D Fomenko. Transport robot with network control system. Advanced Papers of the 4th 1JCA1, Tbilisi, 1975
[5] Dobrotin A B and Lewis R. A practical manipulator system. Proc of the 5th IJCAI, 1997
[6] Belenkov Y O, Gusev S Y, Zotov S Y, Ruzhanskiy Y I, Timofeyev A Y, Frolov Y M and Yakubovich Y A. Adaptive system for control of autonomous mobile robot. Engineer Cybernetics, no.6, 1978
[7] Moravec H P. Robot rover visual navigation. UMI Res Press, 1981
[8] Moravec H P. The Stanford car and the CMU rover. Technical Report of the Robotic Institute at Carnegie-Mellon University, Pittsburgh, 1983
[9] Moravec H P. The CMU rover. Proc of the National Conf on AI, Pittsburgh, 1982
[10] Elfes A and Talukdar S N. Distributed control system for the CMU rover. Report of the Robotic Institute at Carnegie-Mellon University, Pittsburgh, 1983
[11] Giralt G, Sobek R and Chatila R. Multi-level planning and navigation system for a mobile robot: A 1st approach to Hilare. Proc of 6th IJCAI, Tokyo, 1979
[12] Marce L, Julliere M, Place H and Perrichot H. A semi-autonomous remote controlled mobile robot. The Industrial Robot, Dec, 1980
[13] Giralt G, Chatila R and Yaisset M. An integrated navigation and motion control system for autonomous multisensory mobile robots. In Robotic Research, eds. Brady M and Paul R, MIT Press, 1984
[14] Jullire M, Le Flecher J, Marce L, Perrichot H and Place H. Detection simple d'obstacles: Bras tacticle pour robot mobile. Le Nouvel Automatisme, Apr, 1981
[15] Tachi S, Tanie K, Komoziya K, Hosoda H and Abe M. Guide dog robot-Its basic plan and some experiments with MELDOG Mark I. Mechanisms and Machine Theory, 1981,16
[16] Everett H R. A computer controlled sentry robot: A homebuilt project report. Robotics Age, Mar/Apr, 1982
[17] Everett H R. A microprocessor controlled autonomous sentry robot Master's Thesis, Naval Postgraduate Scholl, Oct, 1982
[18] Neff R. Japanese rush to robot production. Electronics, Oct, 1981
[19] Darenberg W, Panic F and Weidemann. Automated guidance system for buses and trucks. Proc of 1st Int Conf on AGYC, 1981
[20] Tajima M and Schneider F. New concepts using robot-vehicles for transport and warehousing system. 4th Int Conf on Automation in Warehousing, Tokyo, 1981
[21] The Bell and Howell family of driverless vehicles(sales pamphlet), Leeland, MI, 1982
[22] Tsugawa S, Yatabe T, Hirose T and Matsumoto S. An automobile with artificial intelligence. Proc IJCAI, 1979
[23] Linden T A, Marsh J P and Dove D L. Architecture and early experience with planning for the ALV. Proc IEEE Int Conf on Robotices and Automation, San Francisco, 1986,3
[24] Kanade T, Thorpe C and Whittaker W. Autonomous vehicle project at CMU. Proc ACM Computer Conf, Cincinnati, Feb, 1986
[25] Chavez R and Meystel A. Structure of intelligence for an autonomous vehicle. Proc of the IEEE Int Conf on Robotics and Automation, Atlanta, 1984
[26] Koch E, Yeh C, Hillel G, Meystel A and Isik C. Simulation of path planning for a system with vision and map updating. Proc of IEEE Int Conf on Robotics and Automation, St Louis, 1985
[27] Meystel A. Primer on autonomous mobility. Drexel Univ, PA, 1986
[28] Parodi A M, Nitao J J and McTamaney L S. An intelligent system for an autonomous vehicle. Proc IEEE Int Conf on Robotics and Automation, San Francisco, 1986,3
[29] Nageswara Rao S Y, Iyengar S S, Jorgensen C C and Weisbin C R. Concurrent algorithms for autonomous robot navigation in an unexplored terrain. Proc IEEE Int Conf on Robotics and Automation, San Francisco, 1986,2
[30] 田华，席裕庚，张钟俊，蒋慰孙．智能机器人系统建模新理论--环递阶模型理论．机器人， 1994，16(3) ：150-158
[31] Saridis G N. Intelligent robot control. IEEE Trans on Auto Cont. 1983,28(5) :547-557
[32] Saridis G N. Machine-Intelligent robots: A hierarchical control approach. Machine Intelligence and Knowledge Engineering for Robotic Applications, Springer-Verlag Berlin Heidelberg, German, 1987(33) : 221-234
[33] Saridis G N. Knowledge implementation: Structures of intelligent control systems. Proc of IEEE Int Sym on Intel Cont, Philadelphia, USA, Jan,1987:9-17
[34] Moed M C and Saridis G N. A boltzmann machine for the organization of intelligent machines. IEEE Trans on Sys, Man and Cyb, 1990,20(5) : 1094-1102
[35] Graham J H and Saridis G N. Linguistic decision structures for hierarchical systems. IEEE Trans on Sys , Man and Cyb, 1982,12(3) :325-333
[36] Saridis G N and Graham J H. Linguistic decision schemata for intelligent robots. Automatic, 1984,20(1) : 121-126
[37] Wang F Y and Saridis G N. A coordination theory for intelligent machines. Automatic, 1990,26(5) :833-844
[38] Saridis G N. Entropy formulation of optimal and adaptive control. IEEE Trans on Auto Cont, 1988,33(8) : 713-721
[39] Albus J S, et al. Hierarchical control for robots and teleoperators. Proc of IEEE Workshop on Intel Cont, Troy, New York, USA, Aug,1985:39-49
[40] Albus I S, et al. Hierarchical control of intelligent machines applied to space station telerobots. Proc of IEEE Int Sym on Intel Cont, Philadelphia, Pennsylvania, USA, Jan, 1987:20-26
[41] Lumia R and Albus J S. An architecture to support teleoperation and autonomy. Proc of IEEE Int Conf on Sys, Man and Cyb, Alexandria, Virginia, USA, Oct, 1987,3:1160-1164
[42] Lumia R and Albus J S. Teleoperation and autonomy for space robotics. Robotics, 1988,4:27-33
[43] Albus J S, et al. NASA/NBS standard reference model for telerobot control system architecture (NASREM), NISTTN 1235, 1989 Ed. (Supersedes NBS TN 1235 Issued July 1987) , Gaithersburg, MD, USA, Apr,1989．
[44] Brooks R A. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 1986,2(1)
[45] Brooks R A. New approach to robotics. Science, 1991,253:1227-1232
[46] Steels L and Brooks R A. The artificial life route to artificial intelligence. Lawrence Earlbaum Associates, Publishers, Hove, U.K, 1995
[47] Mataric M. Integration of representation into goal-driven, behaviour-based robots. IEEE Transaction on Robotics and Automation, 1992,8(3)
[48] 田华，蒋慰孙．智能机器人体系结构的理论研究．控制与决策，1993，8(5) ：330-337
[49]田华，席裕庚，张仲俊，蒋慰孙．智能机器人系统建模的新方法．模式识别与人工智能，1993，6(3)：261-266
[50]Barnes D P. A Behaviour Synthesis Architecture for the control of co-operate mobile robots. Advanced Robotics and Intelligent Machines, Chapter 19, Peter Peregrinus Control Engineering series, ed. Gray J O and Caldwell D
[51]Eustace D, Barnes D, Gray J O. Multiple co-operate mobile robots for unstructured environments. Proceedings of the 1993 International Conference on Advanced Robotics, Tokyo, Nov, 1993
[52]Aylett R Eustace D, Gray J O. Combining predictive and Reactive Control Strategies in Multi-agent System. IEE Press, Proc of the IEE International Conference Control, Warwick, 1994,2
[53]Neves C and Gray J O. System architectures for advanced robotics, lEE Press, Proc of the IEE International Conference Control, 1995,1
[54]冯文镛，杨灿军，陈鹰．物品自动运送机器人(ACR)原型系统总体设计．机电工程，2000,17(4)：54-57
[55]周学才，李卫平，李强．开放式机器人通用控制系统，机器人，1998，20(1)：25-31
[56]范永，谭民．机器人控制器的现状及展望．机器人，1999，21(1)：75-79
[57]饶蚺，邓岳湘，任永益．关于自动导向车最优控制的设计．控制理论与应用，1996，13(6)：838-842
[58]冯建农，柳明，吴捷．基于信息融合的模型小车ANN避障控制．控制理论与应用，1997，14(6)：847-852
[59]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
[60]黄闪，孔毅．智能机器人体系结构与与全局数据库设计与实施．沈阳工业大学学报，1999，21(1)：41-44
[61]Alami R Chatila R, Fleury S, Ghallab M and Ingrand E An architecture for autonomy. The International Journal of Robotics Research, 1998,17(4):315-337
[62]Albus J S. Outline for a theory of intelligence. IEEE Trans on Systems, Man and Cybernetics, 1991,21(3):473-509
[63]Hayes B R. A blackboard architecture for control. Artificial Intelligence, 1985:252-321
[64]Albus J S. Brains, Behavior, and Robotics. Petebourough, NH: BYTE/McGraw-Hill, 1981
[65]Miller G A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psych Rev, 1956,63:71-97
[66]Meystel A. Theoretical foundation of planning and navigation for autonomous robots, lnt J Intelligent Syst, 1987, 2:73-128
[67]Minsky M. A framework for representing knowledge. In The Psychology of Computer Vision, Winston P, Ed. New York: McGraw-Hill, 1975:211-277
[68]Saridis G N. Foundations of the theory of intelligent controls. In Proc IEEE Workshop on Intelligent Control. 1985
[69]Albus J S, McCain H G and Lumia R. NASA/NBS standard reference model for telerobot control system architecture(NASREM). Nat Inst Standards and Tech, Tech Rep 1235, Gaithersburg, MD, 1989
[70]Sacerdoti E D. A structure for plans and behavior. New York: Elsevier, 1977.
[71]Gibson J J. The ecological approach to visual perception. Ithaca, NY: Cornell Univ Press, 1966
[72]Mart D. Vision. San Francisco, CA: Freeman, 1982
[73]Aibus J S and Hong T H. Motion, depth, and image flow. In Proc IEEE Robotics and Automation, Cincinnati, OH, 1990
[74]Kent E and Albus J S. Servoed world models as interfaces between robot control systems and sensory data. Robotica, 1984,2:17-25
[75]Nilsson N J. Principles of artificial intelligence. Springer-Verlag Berlin Heidelberg, New York, 1982
[76]Nilsson N J. Artificial intelligence: A new synthesis. Morgan Kaufmann, CA, 1999
[77]Allen J, Hendler J and Tate A. Reading in planning. Morgan Kaufmann, CA, 1990
[78] Fikes R and Nilsson N. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 1971,2(3) : 189-208
[79] Fikes R, Hart P and Nilsson N. Learning and executing generalized robot plans. Artificial Intelligence, 1972,3(4) :251-288
[80] Sacerdoti E. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 1974,5(2) : 115-135
[81] Sacerdoti E. A structure for plans and behaviours. North-Holland, Amsterdam, 1977
[82] Lesser V and Erman L. A retrospective view of the Hearsay-II architecture. Proc IJCAI-77, Cambridge, MA, 1977:27-35
[83] Stefik M. Planning with constraints. Artificial Intelligence, 1981,16:111-140
[84] Tate A. Generating project networks. Proc IJCAI-77, Cambridge, MA, 1977
[85] Vere S. Planning in time: windows and durations for activities and goals. Trans Patten Anal Mach Intell, 1981,5:246-267
[86] Wilkins D E. Practical planning: Extending the classical AI planning paradigm. San Francisco: Morgan Kaufmann, 1988
[87] Chapman D. Planning for conjunctive goals. Artificial Intelligence, 1987,32(3) :333-377
[88] Currie K and Tate A. O-Plan: the open planning architecture. Artificial Intelligence, 1991,52( 1 ):49-86
[89] Georgeff M P and Lansky. Procedural knowledge. Proc of the IEEE Special Issue on Knowledge Representation, 1986,74:1383-1398
[90] Georgeff M P. Procedural control in production systems. Artificial Intelligence, 1982,18:175-201
[91] Knoblock C A. Automatically generating abstractions for planning. Artificial Intelligence, 1994,68:243-302
[92] Knoblock C A. Learning abstraction hierarchies for problem solving. Proc AAAI-90,1990:923-928
[93] Laborie P and Ghallab M. Planning with sharable resource constraints. Proc IJCAI-95, 1995:1643-1649
[94] Allen J F. An interval-based representation of temporal knowledge. Proc 7th IJCAI, Vancouver, Aug, 1981
[95] Fox M S and Smith S F. ISIS: a knowledge-based system for factory scheduling. Expert Systems, 1984,1(1 ):25-49
[96] Smith S F, Fox M S and Ow P S. Constructing and maintaining detailed production plans: investigations into the development of knowledge-based factory. AI Magazine, 1986,7(4) :45-61
[97] Sadeh N. Look-ahead techniques for micro-opportunistic job shop scheduling. PhD thesis, Carnegie Mellon Univesity, Mar, 1991
[98] Bensana E, Bel G and Dubois. OPAL: a multi-knowledge-based system for industrial job-shop scheduling. International Journal of Production Research, 1988,26(5) :795-819
[99] Erschler J, Lopez P and Thuriot C. Temporal reasoning under resource constraints: application to task scheduling. In Advances in Support Systems Research, 1990:189-194
[100] Beck H. Tosca: A novel approach to the management of job-shop scheduling constraints. In Proc 9th CIM-Europe Annual Conference, Amsterdam, 1993:138-149
[101] Allen J F. Towards a general theory of action and time. Artificial Intelligence, 1984,23:123-154
[102] Shoham Y. Temporal logics in AI: semantical and ontological considerations. Artificial Intelligence, 1987,33:89-104
[103] Ghallab M and Laruelle. Representation and control in Ixtet, a temporal planner. In Proc AIPS-94, 1994:61-67
[104] Ghallab M and Mounir A A. Managing efficiently temporal relations through indexed spanning trees.In Proc UCAI-89, 1989
[105] Ghallab M, Alami R and Chatila R. Dealing with time in planning and execution monitoring. In Robotics Research: The Fourth International Symposium, MIT Press, 1988
[106] Vilain M and Kautz H. Constraint propagation algorithms for temporal reasoning. In Proc of The Fifth National Conference on Artificial Intelligence, Aug, 1986:3 77-3 82
[107] Laffey T J et al. Real-time knowledge-based systems. AI Magazine, 1988,9(1) :27-45
[108] Hayes B R et al. Intelligent monitoring and control. Proc Int Joint Conf Artificial Intelligence, Morgan Kaufmann, San Mateo, Calif, 1989:243-249
[109] Dodhiawala R. Real-time AI systems: a definition and a architecture. Proc Int Joint Conf Artificial Intelligence, Morgan Kaufinann, San Mateo, Calif, 1989:256-261
[110] Kaelbling L P. An architecture for intelligence reactive systems. Proc 1986 Workshop on Reasoning about Actions and Plans, Morgan Kaufmann, San Mateo, Calif, 1987:395-410
[111] Georgeff M P and Ingrand F F. Decision making in an embedded reasoning system. Proc 11th Int Joint Conf Artificial Intelligence, Morgan Kaufmann, San Mateo, Calif, 1989:972-978
[112] Georgeff M P and Ingrand F F. Real-time reasoning: The monitoring and control of spacecraft systems. Proc Sixth IEEE Conf Artificial Intelligence Applications, Computer Society Press, Los Alamitos, Calif, 1990:198-205
[113] Georgeff M P and Ingrand F F. Research on procedure reasoning systems. Tech Report, SRI Int, Menlo Park, Calif, 1990
[114] Wesley L te al. Application of PRS to network management systems. Tech Report, SRI Int, Menlo Park, Calif, 1991
[115] Ingrand F F, Chatila R, Alami R and Robert F. PRS: A high level supervision and control language for autonomous mobile robots. Proc of IEEE Int Conf on Robotics and Automation, 1996:43-49
[116] Ingrand F F and Coutance V. Real-time reasoning using procedural reasoning. Tech Report, LAAS/CNRS, Toulouse, France, 1993:93-104
[117] Ingrand F F, Georgeff M P and Rao A S. An architecture for real-time reasoning and system control. IEEE Expert, 1992:34-44
[118] Firby R J. An investigation into reactive planning in complex domains. Proc Sixth National Conf Artificial Intelligence, MIT Press, Cambridge, Mass, 1987:202-206
[119] Ishida T, Sasaki Y, Nakata K and FukUhara Y. A Meta-level control architecture for production systems. IEEE Trans on Knowledge and Data Engineering, 1995,7(1) :44-52
[120] Munson J H. Robot planning, execution and monitoring in an uncertain environment. In IJCAI-73, Standford California USA, 1973:338-349
[121] Scrinivas S. Error recovery in robot system. PhD thesis, California Institute of Technology, Pasadena, California, 1977
[122] Noreils F R and Chatila. Control of mobile robot actions. In IEEE Int Conf on Robotics and Automation, 1989:701-712
[123] Noreils F R and Chatila R G A general structure for mobile robot action control. IEEE/RSJ International Workshop on Intelligent Robots and Systems, Tsukuba, Japan, 1989:550-556
[124] Noreils F R and Raja G Plan execution monitoring and control architecture for mobile robots. IEEE Trans on Robotics and Automation, 1995,11(2) :255-266
[125] Chatila R, Alami R, Degallaix B and Laruelle H. Integrated planning and execution control of autonomous robot actions. Proc Int Conf on Robotics and Automation, Nice, France, 1992:2689-2696
[126] Noreils F R. Integrating error recovery in a mobile robot control system. IEEE Int Conf Robotics and Automation, 1990:396-401
[127] Fleury S, Herrb M and Chatila R. Design of a modular architecture for autonomous robot. IEEE ICRA'94, San Diego, CA, 1994:3508-3513
[128]Camargo R F, Chatila R and Alami R. Hardware and software architecture for execution control of an autonomous mobile robot. Iht Conf on Industrial Electronics, Control, and Instrumentation, 1992:818-825
[129]Chatila R and Ferraz R. Open architecture design and inter-task/inter-module communication for an autonomous mobile robot. IEEE International Workshop on Intelligent Robots and System, 1990:717-721
[130]Hu T C, Kahng A and Robins G Optimal robust path planning in general environments. IEEE Trans Robotics Automat, 1993,9(6):775-784
[131]Brooks R A. Solving the find path problem by good representation of free space. IEEE Trans Sys, Man, Cybern, 1983,13(3):190-197
[132]Lozano-Perez T and Wesley M. An algorithm for planning collision-free paths among polyhedral obstacles. Communication ACM, 1979, 22 (10): 560-570
[133]Lozano-Perez T. Special planning: a configuration space approach. IEEE Trans Comput, 1983,32(2):108-120
[134]Mataric M J. Integration of representation into goal-driven behavior-based robots. IEEE Trans Robotics Automat, 1992,8(3): 304-312
[135]Sharir M and Schorr A. On shortest path in polyhedral spaces. SIAM J Comput, 1986,15:193-215
[136]Jerez C A, Hashimoto Y, Matsuda T and Tsuchiya T. Environment representation using enclosed obstacles and minimum-turns path planning. Advanced Robotics, 1998,12(4):351-371
[137]Hollaud J H. Adaptation in Natural and Artificial System. MIT Press, Cambridge, Mass, 1975
[138]张晓缋，戴冠中，徐乃平．一种新的优化算法——遗传算法．控制理论与应用，1995，12(3)：265—237
[139]陈根社，陈新海．遗传算法的研究与进展．信息与控制，1994，23(4)：215—222
[140]Goldberg D. Genetic Algorithms in Search, Optimization and Machine Learning. USA: Addsion-Wesley Publishing Company, 1988: 59-308
[141]马钧水，刘贵忠，贾玉兰．改进遗传算法搜索性能的大变异操作．控制理论与应用，1998，6(3)：404—407
[142]孙增圻等编著．智能控制理论与技术．清华大学出版社，1997
[143]Goldberg D E and Lingle R. Alleles, loci, and the Traveling Salesman Problem. Proceeding of an International Conference on Genetic Algorithms and Their Application. 1985:154-159
[144]Davis L. Job Shop Scheduling with Genetic Algorithms. Proceedings of an International Conference on Genetic Algorithms and Their Application. 1985: 136-140
[145]Smith D. Bin Packing with Adaptive Search. Proceedings of an International Conference on Genetic Algorithms and Their Application. 1985:202-206
[146]陈国良等．遗传算法及其应用．人民邮电出版社，1999
[147]恽为民，席裕庚．遗传算法的全局收敛性和计算效率分析．控制理论与应用，1996，13(4)：455-460
[148]孙增圻，张再兴．智能控制的理论与技术．控制与决策，1996，11(1)：1-8
[149]费敏锐，陈伯时，郎文鹏．智能控制方法的交叉综合及其应用．控制理论与应用，1996，13(3)：273-280
[150]西广成．智能控制与相对熵最小化．控制理论与应用，1999，16(1)：27-31
[151]陈建安，戴冠中，徐乃平．学习控制技术·方法和应用的发展方向．控制理论与应用，1999，16(1)：1-5
[152]刘金琨，王树青．多智能体控制系统的设计与实现．控制理论与应用，1999，16(4)：580-582
[153]叶文，杨叔子．基于过程代数的智能机器人系统建模．机器人，1998，20(1)：9-14
[154]谭群华，李伟．基于行为建模的机器人混合模糊-神经控制方法．清华大学学报，1997，37(3)：38-41
[155]张友军，朱淼良，吴春明，胡晓敏．自主式移动机器人系统的体系结构．机器人，1997，19(5)：378-383
[156]Noreils F R. Toward a robot architecture integrating cooperation between mobile robots: application to indoor environment. The International Journal of Robotics Research, 1993,12(1):79-98
[157]Wang F Y and Saridis G N. Task translation and integration specification in intelligent machines. IEEE Trans on Robotics and Automation, 1993,9(3):257-271
[158]Simmons R G Structured control for autonomous robots. IEEE Trans on Robotics and Automation, 1994,10(1):34-43
[159]Watanabe M, Onoguchi K, Kweon I and Kuno Y. Architecture of behavior-based mobile robot in dynamic environment. Proc IEEE Int Conf on Robotics and Automation, Nice, France, 1992
[160]程平，孙茂相，尹朝万，王艳红．基于分层控制的移动机器人最优运动规划．机器人，1997，19(1)：294-299
[161]宫国晖，袁曾任，贾书惠．轮式移动机器人驱动系统的动力学模型的研究．机器人，1997，19(2)：110-115
[162]周捷，翟羽健．基于神经网络的自主车辆导航路径计算．机器人，1999，21(5)：363-368
[163]李强，林良明，颜国正．一种新的移动机器人环境模型．机器人，1999，21(5)：379-385
[164]王军，黄心汉．基于神经网络进化学习的两轮小车避障控制．机器人，1996，18(5)：392-397
[165]张汝波，周宁，顾国昌，张国印．基于强化学习的智能机器人避碰方法研究．机器人，1999，21(3)：204-209
[166]齐国光，郭木河，马少平，归柽华．轮式机器人最优控制的研究．机器人，1996，18(1)：21-27，
[167]孟庆浩，彭商贤，刘大维．基于Q-M图启发式搜索的移动机器人全局路径规划．机器人，1998，20(4)：273-279
[168]梁泉，许晓鸣，张钟俊．多智能体系统协作及控制机理和基本问题分析．控制与决策，1996，11(5)：521-526
[169]梁泉，许晓鸣，何星，张钟俊．多智能体系统智能体协作层的设计与实现．上海交通大学学报，1996，30(4)：59-64
[170]席裕庚，柴天佑，恽为民．遗传算法综述．控制理论与应用，1996，13(6)：697-708
[171]孙艳丰，王众托．遗传算法在优化问题中的应用研究进展．控制与决策，1996，11(4)：425-431
[172]史奎凡，陈月辉．提高遗传算法收敛速度的方法．信息与控制，1998，27(4)：289-293
[173]郑立辉，郭亚军，潘德惠．一种基于梯度搜索的全局优化新算法．控制理论与应用，1997，14(3)：343-347
[174]吴志远，邵惠鹤，吴新余．新的进化过程遗传算法．上海交通大学学报，1997，31(12)：66-68
[175]张晓馈，戴冠中，徐乃平．遗传算法种群多样性分析研究．控制理论与应用，1998，15(12)：17-22
[176]马钧水，刘贵忠，贾玉兰．改进遗传算法搜索性能的大变异操作．控制理论与应用，1998，15(3)：404-407
[177]李书全，赵良英，史智兴，寇纪淞，李敏强．一种防止遗传算法成熟前收敛的有效算法．系统工程理论与实践，1999(5)：72-77
[178]邰宜斌，席裕庚，李秀明．一种机器人路径规划的新方法．上海交通大学学报，1996，30(4)：94-100
[179]张纪会，徐心和．一种新的进化算法——蚁群算法，系统工程理论与实践，1999(3)：84-87
[180]栾尚敏，马绍汉．搜索问题的代数描述及其算法．计算机研究与发展，1997，34(11)：801-806
[181]张德富，顾卫刚．沈平．一种旅行商问题的并行模拟退火算法．计算机研究与发展，1995，32(2)：1-4
[182]姜大立，杨西龙．杜文，周贤伟．车辆路径问题的遗传算法研究．系统工程理论与实践，1999(6)：40-45
[183]Sekhavat S, Svestka P, Laumond J P and Overmars M H. Multilevel path planning for nonholonomic robots using semiholonomic subsystems. The International Journal of Robotics Research, 1998,17(8):840-857
[184]刘书家，赵传强，李朴．虚拟86模式下实时多任务操作系统的研究与实践．研究与发展，1997，34(3)：206-211
[185]徐宝文，李东旭．设计结构图——大型实时软件设计的有效工具．计算机研究与发展，1996，33(4)：256-262
[186]肖兵，沈理．实时控制系统的面向对象软件结构与实现方法．计算机研究与发展，1995，32(8)：49-54
[187]Chochon H. Object-oriented design of mobile robot control system. In 2nd ISER Toulouse, France, Jun, 1991