基于改进的遗传算法和模糊逻辑控制的移动机器人导航

本文给出了一种用遗传算法学习模糊规则以完成移动机器人导航的方法.采用了变长度编码方法和竞争型小生境遗传算法，减少了染色体的尺寸和复杂度，同时提高了学习速度．本文考虑了轮式移动机器人的运动模型，将更符合实际情况的左右轮速度作为模糊规则的输出．整个学习过程在仿真环境下完成后，在仿真和自行开发的全局视觉平台上对学到的规则进行了验证，实验结果证明了方法的正确性．

MOBILE ROBOT NAVIGATION BASED ON IMPROVED GENETIC ALGORITHM AND FUZZY LOGICAL CONTROL

In this paper we propose a learning mechanism for mobile robot navigation. The robot is controlled by a dynamic set of fuzzy rules and the rule set is learned using genetic algorithm. We use messy genetic algorithm to reduce the size and complexity of chromosome and niche genetic algorithm to increase the learning speed. We also take the kinematics model of wheeled mobile robot into account and use the speed of wheels directly as the output of fuzzy rules. After the rules have been learnt in simulated environment, they are tested in the globe vision system designed by us. Experimental results prove the learning mechanism is correct and feasible.