基于自适应动态窗口改进细菌算法与移动机器人路径规划

Mobile robot path planning based on improved Bacteria algorithm and DWA algorithm

针对移动机器人在复杂环境下的路径规划问题,提出一种新的自适应动态窗口改进细菌算法,并将新算法应用于移动机器人路径规划。改进细菌算法继承了细菌算法与动态窗口算法(dynamic window algorithm,DWA)在避障时的优点,能较好实现复杂环境中移动机器人静态和动态避障。该改进算法主要分三步完成移动机器人路径规划。首先,利用改进细菌趋化算法在静态环境中得到初始参考规划路径。接着,基于参考路径,机器人通过自身携带的传感器感知动态障碍物进行动态避障并利用自适应DWA完成局部动态避障路径规划。最后,根据移动机器人局部动态避障完成情况选择算法执行步骤,如果移动机器人能达到最终目标点,结束该算法,否则移动机器人再重回初始路径,直至到达最终目标点。仿真比较实验证明,改进算法无论在收敛速度还是路径规划精确度方面都有明显提升。

To solve the path planning of mobile robots in complex environment,a novel path planning method is proposed which is based on improved bacterial chemotaxis and adaptive dynamic window algorithm.The novel method not only can inherit the advantages of the bacterial chemotaxis and dynamic window algorithm in avoiding obstacle,but also it can be used to avoid the static or dynamic obstacle for mobile robots.This new method can be mainly divided into three steps when it is used for the path planning of mobile robots.Firstly,the improved bacterial chemotaxis algorithm is applied to achieve an initial referential path in a static environment.Secondly,based on the initial planning path,the dynamic obstacle can be avoided by the using sensor and adaptive DWA.In the end,the mobile robot can select the next step based on the given result of avoiding dynamic obstacle.If the robot can accomplish local dynamic avoiding obstacle and reach the final target point,then the algorithm is terminated.Otherwise,the robot will return to the initial path until it reaches the final target point.Simulation results show that the improved algorithm can optimize the convergence speed and accuracy of planning path obviously.