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

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。

A mobile robot path planning method based on adaptive DWA and an improved bacteria algorithm

To solve the problem of mobile robot path planning in complex environments, a novel path planning method based on enhanced bacterial chemotaxis and an adaptive dynamic window algorithm is proposed. In addition to inheriting the benefits of bacterial chemotaxis and the dynamic window algorithm in avoiding obstacles, the novel method can also be used to avoid static or dynamic obstacles for mobile robots. It is typically divided into three steps when employed for mobile robot path planning. First, an initial referential planning path in a static environment is determined using the enhanced bacterial chemotaxis algorithm. Next, based on the reference path, the robot detects and avoids dynamic obstacles using its own sensors and completes path planning for local dynamic obstacle avoidance using adaptive DWA. Ultimately, the mobile robot chooses the next execution step based on the outcome of avoiding dynamic obstacles. If the robot is able to reach the final object point, the algorithm stops. Otherwise, the robot will return to the initial path until the final object point is reached. The comparison of simulation and experiment results demonstrates that the algorithm’s convergence speed and accuracy of path planning have been significantly enhanced.