For a self-reconfigurable robot, how to metamorphose to adapt itself to environment is a difficult problem. To solve this problem, a new relative orientation model which describes modules and their surrounding grids was given, a module motion rules database which enables the robot to avoid obstacles was established, and finally a three-layer planner based on dynamic meta-modules was developed. The firstlayer planner designates the category of each module in robot by evaluation functions and picks out the modules in dynamic meta-modules. The second-layer planner plans the dynamic meta-module path according to output parameters of the first-layer planner. The third-layer planner plans the motion of the modules in dynamic meta-module using topology variation oriented methods. To validate the efficiency of the three-layer planner, two simulations were given. One is the simulation of a single dynamic meta-module, the other is the simulation of planning with an initial configuration composed of 8 modules in complicated environment. Results show that the methods can make robot with any initial configuration move through metamorphosis in complicated environment efficiently.
Configuration information acquisition and matching are two important steps in the self-reconfiguring process of self-reconfigurable robots. The process of configuration information acquisition was introduced, and a self-reconfiguring configuration matching strategy based on graded optimization mechanism was proposed. The first-grade optimization was to search common connection between matching scheme and goal configuration. The second-grade optimization, whose object function was constructed in terms of configuration connectivity, was to search connnon topology according to the results of the first-grade optimization. The entire process of configuration information acquisition and matching was verified by an experiment and genetic algorithm （GA）. The result shows the accuracy of the configuration information acquisition and the effectiveness of the configuration matching method.
针对仅仅依靠初始构形组成模块间的相互协调运动无法完成自重构任务这一情况,结合DL-Cube(Double L Cube)自重构机器人单元模型的结构特性,设计构建了风车形子单元,该子单元具有移动、转变模块方位、携带模块等能力.提出了基于公共拓扑的自重构规划策略,即在进行自重构之前利用分级优化机制搜索出初始构形与目标构形之间的最大公共拓扑,然后以此公共拓扑为目标构形的生长中心,同时,借助于风车形子单元移动、转变模块方位及携带模块的能力,最终实现目标构形的重构.利用仿真实验验证了上述理论的有效性和可行性.
