基于晶体结晶理论的模块化自重构机器人重构策略的研究
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摘要
自重构机器人是由多个模块化机器人单元组成的复杂分布式系统。这些单元能够重新排列构成不同的结构从而适应其所面临的任务。该类机器人具有可扩展性、机器人构形多样性及对环境任务自适应性等诸多特点。特别适合于环境未知、执行任务变化的场合。由于自重构机器人是由许多模块组成,模块间存在多种组合形式,在研究自重构规划策略的同时也为人工智能搜索算法的研究提供了实验检验平台。故而开展模块化自重构机器人方面的研究工作对提高我国在该领域的科研水平、扩展机器人的应用背景等具有重要的理论与现实意义。相应地,此种机器人的提出在机器人本体结构设计、自重构理论和运动规划层面上也带来了新的问题及挑战。
自重构机器人本体是研究自重构机器人相关内容的基础,它的结构特征直接影响自重构理论模型的建立及机器人运动的形式。本文在分析晶体基本组成单元晶胞的结构特征及自重构机器人所需功能的前提下,研制了一种单转动自由度立方晶胞单元模块,该类模块的两个“L”形构件依靠舵机连接在一起。模块有主动模块与被动模块之分,主动模块与被动模块间采用直流减速微电机与旋转钩孔的平面自锁连接机构实现连接。为了清晰直观地获知机器人构形的整体结构,采用拓扑学理论描述机器人的构形。而在计算机处理机器人构形信息时,采用一个含有10个元素的数组描述单个模块的空间方位,利用节点连接表和拓扑连接表记录构形中存在的连接及连接方位,可准确地描述机器人的空间位姿及模块间的连接关系。
自重构机器人自重构理论是其重要的研究内容之一,同时也是影响自重构机器人能否应用到实际生活中的重要因素之一。为了利用晶体结晶理论清晰直观地描述自重构机器人的自重构过程,在对晶体结晶过程与自重构过程之间的相似之处进行分析对比之后,本文对自重构机器人的仿结晶自重构过程进行了分析:在构建目标构形过程中,首先确立生长中心、模块方位转换中心及模块放置中心,然后借助于晶体结晶层生长理论确定目标构形的生长过程,最后利用模块组辅助单个模块完成模块的传送和方位转变的过程。同时,本文建立了面向立方晶胞机器人系统的仿晶体结晶自重构过程模型。该模型除了遵守仿晶体结晶自重构过程的一般步骤之外,在目标构形生长方向、各种待生长位置优先级、模块组的形状、目标模块传送、放置及方位转变方式等方面进行了具体的定义和规划设计。所设计的模块组为“风车”形晶胞群,该模块组所具备的移动、重构、携带模块及改变模块方位等功能能够辅助单元模块实现类似分子的自由运动。
在处理如何实现状态转换这类问题时,通常采取的办法是首先找到两种状态均具有的共同之处,然后在此基础之上再寻求具体的问题解决方案,采用此种方法能够将问题简化,同时,最重要的是能够提高解决问题的效率。借鉴此种解决问题的思想,针对初始构形与目标构形模块组成数量相对较少的情况,本文提出了基于籽晶与层生长理论的自重构规划策略,即利用智能搜索算法寻找到初始构形与目标构形之间的最大公共拓扑,并将其作为构建目标构形的生长籽晶,然后在此基础上实现目标构形的构建。构建目标构形时,仍采用晶体结晶层生长理论确定模块的生长顺序和借助于“风车”形晶胞群完成目标模块传送和方位转变任务。
移动是机器人所应具备的基本功能之一,而大部分自重构机器人单元模块恰恰不具备此种功能,如果期望自重构机器人能够移动,必须将多个模块组合在一起才能实现。通过规划模块的连接、断开和运动顺序发现,由“风车”形晶胞群组成的机器人构形具备重构移动功能。针对运动平面内是否存在障碍物,本文分别采用总体势下降法和启发快速扩展随机树搜索方法对由“风车”形晶胞群组成的机器人构形的运动路径进行了规划。机器人的每一步运动利用内置的固定运动序列库实现。
本文基于立方晶胞自重构机器人系统对“风车”形晶胞群所具备的基本功能进行了实验验证。实验结果表明,所构建的“风车”形晶胞群能够实现移动、重构、携带以及改变其他模块方位等功能。利用仿真实验系统对所建立的面向立方晶胞机器人系统的仿晶体结晶自重构过程模型进行了验证,目标构形分别设为由4个“风车”形晶胞群组成的机器人构形以及“H”构形,实验结果表明利用该理论执行自重构任务是有效可行的。
A modular self-reconfigurable robot (MSR) is a complex distributed system which is composed of multiple modular robotic units. These units can be rearranged to form different structures for adapting to specific tasks. This kind of robot has many merits, such as extensibility, variety of robot’s configuration, and adaptation to the environment and tasks. MSR is especially suitable for the situations of unknown environment and changing tasks. MSR is composed of many modules, which can be combined in many different ways. Hence, it provides an ideal platform for checking self-reconfiguring planning strategy and artificial intelligence (AI) searching algorithm. Therefore, the research on the MSR system has the important theoretical and practical significance to enhance the research level and extend the application backgrounds. Meanwhile, some new problems about the robot structure design, self-reconfiguring theory and motion planning come with the proposed MSR.
MSR body is the foundation for studying all the related contents in MSR research. Its structural characteristics directly affect the robot’s motion mode and the establishment of the self-reconfiguring theoretical model. After analysis on structural properties of essential unit cell and the functions needed for MSR, a novel cubic crystal cell module with one degree of freedom (DOF) is developed, which is composed of double L-like components. Double L-like components of a cubic crystal cell module are linked by a server motor. Modules are divided into active and passive categories. Active modules and passive modules can be connected by a planar self-locking connecting mechanism. Such mechanism consists of rotating hooks, hole and direct-current deceleration micro-motor. In order to get the whole structure of the robot configuration visually and clearly, the robot configuration is described by topological theory. When a computer deals with the robot configuration information, the spatial position of a module is described by an array which contains ten elements. Connections and connection azimuths between two arbitrary connecting modules are recorded in the configuration by a topology connection table and a node connection table, in which the spatial position of the robot and the connection relationship between the modules are expressed exactly.
Self-reconfiguring theory is an important research domain in the study of self-reconfigurable robot, and is a key factor which has influence on whether MSR can be applied in practice or not. In order to describe self-reconfiguring process according to crystal’s crystallization theory, after analysis on the similarities between crystal crystallization process and the self-reconfigurable process, this paper analyzed self-reconfiguring process of self-reconfigurable robots simulating crystal’s crystallization. Firstly, the growth center, the center for changing module’s azimuth and the module placement center are set up. Secondly, the growth process of goal configuration is determined by layer growth theory of crystal crystallization. Finally, the moving process and changing azimuth of the module are realized by single module that is assisted by meta-module. At the same time, the self-reconfiguring process model based on crystal’s crystallization oriented to cubic cell robot system is established. The model complies with general execution steps of self-reconfiguring process simulating crystal’s crystallization. Besides, the growth direction, the priority of positions to grow, the shape of meta-module, the method of transferring goal module, the method of placing goal module and the method of changing azimuth of goal module are planned in detail. The designed meta-module is called windmill-like crystal cell group (WLCCG). It has the abilities of moving, self-reconfiguring, changing azimuth of the other modules and carrying other modules. These abilities are helpful for the module to realize free motion. The motion is similar to the molecular motion.
When the problem about how to realize transition between two different states are handled, the methods usually adopted are as follows: Firstly, find some common points between two states. Secondly, seek some concrete schemes of solving problem. Thus some problems can be simplified by this method and it is most important that the efficiency of problem solving can be improved. Aiming to the case that the numbers of modules in the initial and goal configuration are relatively few, self-reconfigurable planning strategy based on seed crystal and layer growth theory is proposed using the philosophies of the methods introduced above. Firstly, the most common topology between initial and goal configuration is searched out by intelligent searching algorithm and is used as growth seed crystal of goal configuration. Secondly, the goal configuration is constructed based on the common topology. In the process of constructing goal configuration, the growth sequence of modules is determined by layer growth theory of crystal’s crystallization, and the tasks of transmitting and changing azimuth of the goal module is implemented by WLCCG.
Motion is a basic ability of robot, most MSRs’unit modules do not have this ability. Only when many modules are connected together, self-reconfigurable robot has the ability of moving. In the process of planning the sequence of module connecting, disconnecting and rotating, it was found that the robot configuration made of four WLCCGs can realize reconfigurable moving function. If there are no obstacles on a moving plane, the algorithm based on the gradient of the potential field is used in planning the moving path of robot composed of WLCCGs. If there are obstacles on a moving plane, the algorithm of heuristics rapidly-exploring random tree is chosen. The actions of robot are realized by fixed motion sequence database.
Finally, the basic functions of WLCCG were verified by cubic crystal cell self-reconfigurable robot system. The experiments results show that the WLCCG can realize the functions of moving, self-reconfiguring, changing azimuth of the other modules and carrying the other modules. The effectiveness of the self-reconfiguring process model based on crystal’s crystallization oriented to cubic cell robot system is verified by a simulation experiment. The goal configurations are composed of four MLMMs or H-like configuration. The results show that the theory presented in the chapter above is feasible and effective.
自重构机器人本体是研究自重构机器人相关内容的基础,它的结构特征直接影响自重构理论模型的建立及机器人运动的形式。本文在分析晶体基本组成单元晶胞的结构特征及自重构机器人所需功能的前提下,研制了一种单转动自由度立方晶胞单元模块,该类模块的两个“L”形构件依靠舵机连接在一起。模块有主动模块与被动模块之分,主动模块与被动模块间采用直流减速微电机与旋转钩孔的平面自锁连接机构实现连接。为了清晰直观地获知机器人构形的整体结构,采用拓扑学理论描述机器人的构形。而在计算机处理机器人构形信息时,采用一个含有10个元素的数组描述单个模块的空间方位,利用节点连接表和拓扑连接表记录构形中存在的连接及连接方位,可准确地描述机器人的空间位姿及模块间的连接关系。
自重构机器人自重构理论是其重要的研究内容之一,同时也是影响自重构机器人能否应用到实际生活中的重要因素之一。为了利用晶体结晶理论清晰直观地描述自重构机器人的自重构过程,在对晶体结晶过程与自重构过程之间的相似之处进行分析对比之后,本文对自重构机器人的仿结晶自重构过程进行了分析:在构建目标构形过程中,首先确立生长中心、模块方位转换中心及模块放置中心,然后借助于晶体结晶层生长理论确定目标构形的生长过程,最后利用模块组辅助单个模块完成模块的传送和方位转变的过程。同时,本文建立了面向立方晶胞机器人系统的仿晶体结晶自重构过程模型。该模型除了遵守仿晶体结晶自重构过程的一般步骤之外,在目标构形生长方向、各种待生长位置优先级、模块组的形状、目标模块传送、放置及方位转变方式等方面进行了具体的定义和规划设计。所设计的模块组为“风车”形晶胞群,该模块组所具备的移动、重构、携带模块及改变模块方位等功能能够辅助单元模块实现类似分子的自由运动。
在处理如何实现状态转换这类问题时,通常采取的办法是首先找到两种状态均具有的共同之处,然后在此基础之上再寻求具体的问题解决方案,采用此种方法能够将问题简化,同时,最重要的是能够提高解决问题的效率。借鉴此种解决问题的思想,针对初始构形与目标构形模块组成数量相对较少的情况,本文提出了基于籽晶与层生长理论的自重构规划策略,即利用智能搜索算法寻找到初始构形与目标构形之间的最大公共拓扑,并将其作为构建目标构形的生长籽晶,然后在此基础上实现目标构形的构建。构建目标构形时,仍采用晶体结晶层生长理论确定模块的生长顺序和借助于“风车”形晶胞群完成目标模块传送和方位转变任务。
移动是机器人所应具备的基本功能之一,而大部分自重构机器人单元模块恰恰不具备此种功能,如果期望自重构机器人能够移动,必须将多个模块组合在一起才能实现。通过规划模块的连接、断开和运动顺序发现,由“风车”形晶胞群组成的机器人构形具备重构移动功能。针对运动平面内是否存在障碍物,本文分别采用总体势下降法和启发快速扩展随机树搜索方法对由“风车”形晶胞群组成的机器人构形的运动路径进行了规划。机器人的每一步运动利用内置的固定运动序列库实现。
本文基于立方晶胞自重构机器人系统对“风车”形晶胞群所具备的基本功能进行了实验验证。实验结果表明,所构建的“风车”形晶胞群能够实现移动、重构、携带以及改变其他模块方位等功能。利用仿真实验系统对所建立的面向立方晶胞机器人系统的仿晶体结晶自重构过程模型进行了验证,目标构形分别设为由4个“风车”形晶胞群组成的机器人构形以及“H”构形,实验结果表明利用该理论执行自重构任务是有效可行的。
A modular self-reconfigurable robot (MSR) is a complex distributed system which is composed of multiple modular robotic units. These units can be rearranged to form different structures for adapting to specific tasks. This kind of robot has many merits, such as extensibility, variety of robot’s configuration, and adaptation to the environment and tasks. MSR is especially suitable for the situations of unknown environment and changing tasks. MSR is composed of many modules, which can be combined in many different ways. Hence, it provides an ideal platform for checking self-reconfiguring planning strategy and artificial intelligence (AI) searching algorithm. Therefore, the research on the MSR system has the important theoretical and practical significance to enhance the research level and extend the application backgrounds. Meanwhile, some new problems about the robot structure design, self-reconfiguring theory and motion planning come with the proposed MSR.
MSR body is the foundation for studying all the related contents in MSR research. Its structural characteristics directly affect the robot’s motion mode and the establishment of the self-reconfiguring theoretical model. After analysis on structural properties of essential unit cell and the functions needed for MSR, a novel cubic crystal cell module with one degree of freedom (DOF) is developed, which is composed of double L-like components. Double L-like components of a cubic crystal cell module are linked by a server motor. Modules are divided into active and passive categories. Active modules and passive modules can be connected by a planar self-locking connecting mechanism. Such mechanism consists of rotating hooks, hole and direct-current deceleration micro-motor. In order to get the whole structure of the robot configuration visually and clearly, the robot configuration is described by topological theory. When a computer deals with the robot configuration information, the spatial position of a module is described by an array which contains ten elements. Connections and connection azimuths between two arbitrary connecting modules are recorded in the configuration by a topology connection table and a node connection table, in which the spatial position of the robot and the connection relationship between the modules are expressed exactly.
Self-reconfiguring theory is an important research domain in the study of self-reconfigurable robot, and is a key factor which has influence on whether MSR can be applied in practice or not. In order to describe self-reconfiguring process according to crystal’s crystallization theory, after analysis on the similarities between crystal crystallization process and the self-reconfigurable process, this paper analyzed self-reconfiguring process of self-reconfigurable robots simulating crystal’s crystallization. Firstly, the growth center, the center for changing module’s azimuth and the module placement center are set up. Secondly, the growth process of goal configuration is determined by layer growth theory of crystal crystallization. Finally, the moving process and changing azimuth of the module are realized by single module that is assisted by meta-module. At the same time, the self-reconfiguring process model based on crystal’s crystallization oriented to cubic cell robot system is established. The model complies with general execution steps of self-reconfiguring process simulating crystal’s crystallization. Besides, the growth direction, the priority of positions to grow, the shape of meta-module, the method of transferring goal module, the method of placing goal module and the method of changing azimuth of goal module are planned in detail. The designed meta-module is called windmill-like crystal cell group (WLCCG). It has the abilities of moving, self-reconfiguring, changing azimuth of the other modules and carrying other modules. These abilities are helpful for the module to realize free motion. The motion is similar to the molecular motion.
When the problem about how to realize transition between two different states are handled, the methods usually adopted are as follows: Firstly, find some common points between two states. Secondly, seek some concrete schemes of solving problem. Thus some problems can be simplified by this method and it is most important that the efficiency of problem solving can be improved. Aiming to the case that the numbers of modules in the initial and goal configuration are relatively few, self-reconfigurable planning strategy based on seed crystal and layer growth theory is proposed using the philosophies of the methods introduced above. Firstly, the most common topology between initial and goal configuration is searched out by intelligent searching algorithm and is used as growth seed crystal of goal configuration. Secondly, the goal configuration is constructed based on the common topology. In the process of constructing goal configuration, the growth sequence of modules is determined by layer growth theory of crystal’s crystallization, and the tasks of transmitting and changing azimuth of the goal module is implemented by WLCCG.
Motion is a basic ability of robot, most MSRs’unit modules do not have this ability. Only when many modules are connected together, self-reconfigurable robot has the ability of moving. In the process of planning the sequence of module connecting, disconnecting and rotating, it was found that the robot configuration made of four WLCCGs can realize reconfigurable moving function. If there are no obstacles on a moving plane, the algorithm based on the gradient of the potential field is used in planning the moving path of robot composed of WLCCGs. If there are obstacles on a moving plane, the algorithm of heuristics rapidly-exploring random tree is chosen. The actions of robot are realized by fixed motion sequence database.
Finally, the basic functions of WLCCG were verified by cubic crystal cell self-reconfigurable robot system. The experiments results show that the WLCCG can realize the functions of moving, self-reconfiguring, changing azimuth of the other modules and carrying the other modules. The effectiveness of the self-reconfiguring process model based on crystal’s crystallization oriented to cubic cell robot system is verified by a simulation experiment. The goal configurations are composed of four MLMMs or H-like configuration. The results show that the theory presented in the chapter above is feasible and effective.
引文
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26 C.ünsal, P. K. Khosla. A Multi-layered Planner for Self-reconfiguration of a Uniform Group of I-Cube Modules. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Mani, Hawaii, 2001: 598-605
27 H. Kurokawa, A. Kamimura, E. Yoshida et al. Self-reconfigurable modular robot (M-TRAN) and its motion design. 7th International Conference on Control, Automation, Robotics and Vision. Singapore, 2001: 51-56
28 S. Murata, E. Yoshida, A. Kamimura, et al. M-TRAN Self-reconfigurable Modular Robot System. IEEE/ASME Transactions on Mechatroncis, 2002, 7(4): 431-441
29 E. Yoshida, S. Murata, A. Kamimura, et al. A Self-reconfigurable Modular Robot: Reconfiguration Planning and Experiments. International Journal of Robotics Research, 2003, 21(10): 903-916
30 K. C. Prevas, C.ünsal, M. ?. Efe et al. A Hierarchical Motion Planning Strategy for a Uniform Self-Reconfigurable Modular Robotic System. Prodeedings of the IEEE International Conference on Robotics and Automation, Washington, DC. 2002: 314-320
31 A. Kamimura, S. Murata, E. Yoshida et al. Self-reconfigurable Modular Robot: Experiments on Reconfiguration and Locomotion. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001: 606-612
32 E. Yoshida, S. Murata, A. Kamimura et al. A Motion Planning Method for a Self-Reconfigurable Modular Robot. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001: 590-597
33 E. Yoshida, S. Murata, A. Kamimura et al. Motion Planning for a Self-Reconfigurable Modular Robot. Experimental Robotics. 2001, VII: 385-394
34 E. Yoshida, S. Murata, H. Kurokawa et al. Self-reconfigurable Modular Robots: Hardware and Software Development in AIST. Proceedings of the IEEE International Conference on Robotics, Intelligent System and Signal Processing, Changsha, China, 2003: 339-346
35 H. Kurakawa, A. Kamimura, S. Murata et al. M-TRAN II: Metamorphosis from a Four-Legged Walker to a Caterpillar. Proceedings of the IEEE/RSJInternational Conference on Intelligent Robots and Systems. Las Vegas, Nevada, 2003: 2454~2459
36 A. Kamimura, H. Kurokawa, E. Yoshida et al. Automatic Locomotion Pattern Generation for Modular Robots. Prodeedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2003: 714-720
37 E. Yoshida, S. Murata, A. Kamimura et al. Evolutionary Synthesis of Dynamic Motion and Reconfiguration Process for A Modular Robot M-TRAN. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 2003: 1004-1010
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39 Q. X. Wu, Y. H. Wang, Y. Q. Fei. Motion Simulation and Experiment of a Novel Modular Self-reconfigurable Robot. Journal of Southeast University(English Edition), 2006, 22(2):185-190
40吴秋轩,曹广益,费燕琼.全局信息分布式控制的自重构机器人运动研究.辽宁工程技术大学学报, 2006, 25(5):727-730
41 Q. X. Wu, G. Y. Cao, Y. Q. Fei. Described Model of A ModularSe lf-R ec onfigurable Robot. Proceedings of International Conference on Machine Learning and Cybernetics. Guangzhou, China, 2005:182-187
42 Q. X. Wu, Y. M. He, G. Y. Cao. Investigation on the System Design of Automatic Inspection Robot and its Motion Adjustment Algorithm. International Journal Information Technology, 2006, 11(5):87-93
43吴秋轩,曹广益,费燕琼.基于多智能体自重构机器人突现控制系统平台的构建.高技术通讯, 2006, 16(3):246-251
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45 Q. X. Wu, G. Y. Cao, H. Y. Tian, et al. Study on Distributed Motion of Self-reconfigurable Robot based on Local Rules. Journal of Shanghai Jiaotong University (Science), 2007, 12(2): 164-171
46 D. Rus, M. Vona. A Basis for Self-reconfiguring Robots Using Crystal Modules. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2000: 2194-2202
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48 D. Rus, M. Vona. A Physical Implementation of the Self-reconfiguring Crystalline Robot. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, 2000: 1726-1733
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24 C.ünsal, H. Kili???te, P. K. Khosla. A Modular Self-reconfigurable Bipartite Robotic System: Implementation and Motion Planning. Autonomous Robots, 2001, 10(1): 23–40
25 C.ünsal, H. Kili???te, M. Patton et al. Motion Planning for a Modular Self-reconfiguring Rrobotic System. 5th International Symposium on Distributed Autonomous Robotic Systems, Knoxville, TN, 2000: 661-670
26 C.ünsal, P. K. Khosla. A Multi-layered Planner for Self-reconfiguration of a Uniform Group of I-Cube Modules. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Mani, Hawaii, 2001: 598-605
27 H. Kurokawa, A. Kamimura, E. Yoshida et al. Self-reconfigurable modular robot (M-TRAN) and its motion design. 7th International Conference on Control, Automation, Robotics and Vision. Singapore, 2001: 51-56
28 S. Murata, E. Yoshida, A. Kamimura, et al. M-TRAN Self-reconfigurable Modular Robot System. IEEE/ASME Transactions on Mechatroncis, 2002, 7(4): 431-441
29 E. Yoshida, S. Murata, A. Kamimura, et al. A Self-reconfigurable Modular Robot: Reconfiguration Planning and Experiments. International Journal of Robotics Research, 2003, 21(10): 903-916
30 K. C. Prevas, C.ünsal, M. ?. Efe et al. A Hierarchical Motion Planning Strategy for a Uniform Self-Reconfigurable Modular Robotic System. Prodeedings of the IEEE International Conference on Robotics and Automation, Washington, DC. 2002: 314-320
31 A. Kamimura, S. Murata, E. Yoshida et al. Self-reconfigurable Modular Robot: Experiments on Reconfiguration and Locomotion. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001: 606-612
32 E. Yoshida, S. Murata, A. Kamimura et al. A Motion Planning Method for a Self-Reconfigurable Modular Robot. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001: 590-597
33 E. Yoshida, S. Murata, A. Kamimura et al. Motion Planning for a Self-Reconfigurable Modular Robot. Experimental Robotics. 2001, VII: 385-394
34 E. Yoshida, S. Murata, H. Kurokawa et al. Self-reconfigurable Modular Robots: Hardware and Software Development in AIST. Proceedings of the IEEE International Conference on Robotics, Intelligent System and Signal Processing, Changsha, China, 2003: 339-346
35 H. Kurakawa, A. Kamimura, S. Murata et al. M-TRAN II: Metamorphosis from a Four-Legged Walker to a Caterpillar. Proceedings of the IEEE/RSJInternational Conference on Intelligent Robots and Systems. Las Vegas, Nevada, 2003: 2454~2459
36 A. Kamimura, H. Kurokawa, E. Yoshida et al. Automatic Locomotion Pattern Generation for Modular Robots. Prodeedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2003: 714-720
37 E. Yoshida, S. Murata, A. Kamimura et al. Evolutionary Synthesis of Dynamic Motion and Reconfiguration Process for A Modular Robot M-TRAN. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 2003: 1004-1010
38 Q. X. Wu, Y. H. Wang, G. Y. Cao, et al. Locomotion Control of Distributed Self-reconfigurable Robot based on Cellular Automata. Proceedings of Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005. Hefei, China, 2005:179-188
39 Q. X. Wu, Y. H. Wang, Y. Q. Fei. Motion Simulation and Experiment of a Novel Modular Self-reconfigurable Robot. Journal of Southeast University(English Edition), 2006, 22(2):185-190
40吴秋轩,曹广益,费燕琼.全局信息分布式控制的自重构机器人运动研究.辽宁工程技术大学学报, 2006, 25(5):727-730
41 Q. X. Wu, G. Y. Cao, Y. Q. Fei. Described Model of A ModularSe lf-R ec onfigurable Robot. Proceedings of International Conference on Machine Learning and Cybernetics. Guangzhou, China, 2005:182-187
42 Q. X. Wu, Y. M. He, G. Y. Cao. Investigation on the System Design of Automatic Inspection Robot and its Motion Adjustment Algorithm. International Journal Information Technology, 2006, 11(5):87-93
43吴秋轩,曹广益,费燕琼.基于多智能体自重构机器人突现控制系统平台的构建.高技术通讯, 2006, 16(3):246-251
44 Q. X. Wu, G. Y. Cao, Y. Q. Fei. Research on Metamorphic Algorithm of Modular Self-reconfigurable Robots Based Cellular Automata. Proceeding of International Conference on Machine Learning and Cybernetics. Guangzhou, China, 2005:585-590
45 Q. X. Wu, G. Y. Cao, H. Y. Tian, et al. Study on Distributed Motion of Self-reconfigurable Robot based on Local Rules. Journal of Shanghai Jiaotong University (Science), 2007, 12(2): 164-171
46 D. Rus, M. Vona. A Basis for Self-reconfiguring Robots Using Crystal Modules. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 2000: 2194-2202
47 D. Rus, M. Vona. Self-reconfiguration Planning with Compressible Unit Modules. Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, Michigan, 1999: 2513-2520
48 D. Rus, M. Vona. A Physical Implementation of the Self-reconfiguring Crystalline Robot. Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, 2000: 1726-1733
49 Z. Bulter, R. Fitch, D. Rus. Experiments in Distributed Locomotion with A Unit-compressible Modular Robot. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002: 2813-2818
50 Z. Bulter, R. Fitch, D. Rus, et al. Distributed Goal Recognition Algorithms for Modular Robots. Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC, 2002: 110-116
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