基于随机行走的群机器人二维地图构建

机器人在未知环境自主探索时，需要快速准确地获取环境地图信息。针对高效探索和未知环境的地图构建问题，将随机行走算法应用于群机器人的探索中，机器人模拟布朗运动，对搜索区域建图。然后，改进了布朗运动算法，通过设置机器人随机行走时的最大旋转角度，来避免机器人重复性地搜索一个区域，使机器人在相同时间内探索更多的区域，提高机器人的搜索效率。最后，通过搭载激光雷达的多个移动机器人进行了仿真实验，实验分析了最大转角增量、机器人数量以及机器人运动步数对搜索区域的影响。

Two-dimensional mapping of swarm robot based on random walk

Robots need to quickly and accurately obtain environmental map information when exploring unknown environments autonomously. For the problems of efficient exploration and map construction of unknown environments, the random walk algorithm was applied to the exploration of swarm robots, which simulate Brownian motion and build maps of the searched area. Then, the Brownian motion algorithm was improved to avoid the robot to search a region repeatedly by setting the maximum rotation angle when the robot walks randomly, so that the robot was able to explore more areas in the same time and the search efficiency of the robot was improved. Finally, simulation experiments were carried out through a group of mobile robots equipped with lidar, the influences of maximum rotation angle increment, the number of robots and movement steps of robot on the search area were analyzed.