基于进化算法和模糊控制的机器人路径规划

Research on Path Planning of Robot Based on Evolutionary Algorithm and Fuzzy Control Algorithm

机器人局部路径规划是路径规划问题的典型应用,局部路径规划是在环境信息未知的情况下,依靠传感器采集环境信息。提出一种进化算法和模糊控制算法相结合的智能路径规划策略,首先通过机器人上的图像传感器得到相应环境信息,然后采用遗传规划算法(GP)对移动机器人的环境信息进行识别和分类,得到全局最优解。分类结果有助于区分障碍物和目标,进一步利用模糊推理将障碍物和目标的未知信息模糊化并建立模糊规则库,建立的模糊规则库大大缩小。最后,通过解模糊产生驱动命令,移动机器人在驱动命令指挥下选择最优路径到达指定地点。仿真结果表明,提出的智能路径规划策略可以使移动机器人对未知环境信息的分类更加准确,识别更加高效。通过将遗传规划分类算法与蚁群算法(ACO)的收敛特性进行比较,以及与蚁群算法和模糊控制方法的最优搜索路径相比较,结果表明提出的算法具有较高的运算效率和可靠性。

The local path planning of mobile robot is a typical application of path planning problem.Local path planning relies on sensors to acquire environment information when the environment information is unknown.In this paper,we propose an intelligent path planning strategy combined evolutionary algorithm and fuzzy control algorithm. The corresponding environmental information is obtained by the image sensor on the robot.Then the environmental information of the mobile robot is identified and classified by using the genetic programming（ GP）.The global optimal solution can be avoided to distinguish obstacles and goals correctly.Fuzzy reasoning is used to blur the position and the target position information.The fuzzy rules are set up and the precise drive commands are generated by solving the fuzzy rules.The simulation shows that the path planning method proposed can realize the accurate identification and classification of the unknown environment.In comparison with the classification algorithm of GA and the ant colony optimization（ ACO）,and compared with the optimal search path by colony optimization and pure fuzzy control algorithm,the proposed method has high efficiency and reliability.