黄宜庆, 王正刚, 王徽, 葛愿. 基于边缘梯度算法的多移动机器人协作地图构建[J]. 信息与控制, 2020, 49(1): 62-68. DOI: 10.13976/j.cnki.xk.2020.8651
引用本文: 黄宜庆, 王正刚, 王徽, 葛愿. 基于边缘梯度算法的多移动机器人协作地图构建[J]. 信息与控制, 2020, 49(1): 62-68. DOI: 10.13976/j.cnki.xk.2020.8651
HUANG Yiqing, WANG Zhenggang, WANG Hui, GE Yuan. Cooperative Mapping for Multiple Mobile Robots via Edge Gradient Algorithm[J]. INFORMATION AND CONTROL, 2020, 49(1): 62-68. DOI: 10.13976/j.cnki.xk.2020.8651
Citation: HUANG Yiqing, WANG Zhenggang, WANG Hui, GE Yuan. Cooperative Mapping for Multiple Mobile Robots via Edge Gradient Algorithm[J]. INFORMATION AND CONTROL, 2020, 49(1): 62-68. DOI: 10.13976/j.cnki.xk.2020.8651

基于边缘梯度算法的多移动机器人协作地图构建

Cooperative Mapping for Multiple Mobile Robots via Edge Gradient Algorithm

  • 摘要: 为了提高未知环境下移动机器人构建地图的效率,提出了一种基于边缘梯度算法(EGA,Edge gradient algorithm)的多机器人协作地图构建方法.将未知环境划分成4个区域,以分布式分配方式对多移动机器人系统分配不同的搜索任务,有效避免了多移动机器人陷入凹型障碍物区域.该算法能够使移动机器人在远离障碍物时具有较大的视野,而靠近障碍物时视野因障碍物遮挡而变窄,从而获得更加精确的地图构建数据.通过围绕障碍物运动来采集障碍物的相对坐标信息,从而构建未知环境的全局地图,在理论上系统分析了边缘梯度算法的收敛性.最后,通过多组仿真实验,验证了本文方法能够高效快速地完成未知环境障碍物的地图构建.

     

    Abstract: This study aimed to improve the efficiency of map building for mobile robots in an unknown environment.Thus, a cooperative mapping method for multiple mobile robots via edge gradient algorithm is presented in this study.The unknown environment is divided into four regions, and different search tasks are assigned to the multiple mobile robot system by the distributed task allocation method, which can effectively prevent multiple mobile robots from falling into concave obstacle areas.The mobile robot can have a large field of view when moving away from obstacles and a small field of view when approaching obstacles.The global map of the unknown environment is constructed by collecting the relative coordinate information of obstacles by moving around them.Therefore, the obtained map-building data are relatively accurate.The convergence analysis of the edge gradient algorithm is presented.Finally, multiple groups of simulation experiments verified that the proposed method can be used to efficiently build a complete map of the unknown environment.

     

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