Evolution of fuzzy behaviors for multi-robotic system
Evolution of fuzzy behaviors for multi-robotic system
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DOI：
10.1016/j.robot.2006.07.005
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发表时间：
2007-02
期刊：
影响因子：
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通讯作者：
P. Vadakkepat;Xiao Peng;B. Quek;Tong-heng Lee
中科院分区：
文献类型：
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作者：
P. Vadakkepat;Xiao Peng;B. Quek;Tong-heng Lee
在多机器人系统中，机器人在动态变化的环境中相互交互。机器人需要在个人和群体层面上都具有智能。在本文中，一个模糊的基于行为的体系结构的演变进行了讨论。基于行为的体系结构将多个机器人之间复杂的交互分解为不同复杂程度的模块化行为。模糊逻辑方法将类似人类的推理引入行为的构建、选择和协调。基于模糊行为的体系结构中的各种行为是通过遗传算法（GA）进化的。在最低层次的结构层次，进化的模糊控制器提高了原始机器人动作的平滑性和准确性。在更高的层次上，进化后的个体机器人行为变得更加熟练。在最高层次上，进化的群体行为导致了积极的竞争战略。仿真和真实世界的机器人足球系统的实验证明了该方法的有效性。
In a multi-robotic system, robots interact with each other in a dynamically changing environment. The robots need to be intelligent both at the individual and group levels. In this paper, the evolution of a fuzzy behavior-based architecture is discussed. The behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. The fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. Various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (GA). At the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. At a higher level, the individual robot behaviors have become more skillful after the evolution. At the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. The simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.