李秀智, 赫亚磊, 孙炎珺, 张祥银, 张晓凡. 基于复合式协同策略的移动机器人自主探索[J]. 机器人, 2021, 43(1): 44-53. DOI: 10.13973/j.cnki.robot.200009
引用本文: 李秀智, 赫亚磊, 孙炎珺, 张祥银, 张晓凡. 基于复合式协同策略的移动机器人自主探索[J]. 机器人, 2021, 43(1): 44-53. DOI: 10.13973/j.cnki.robot.200009
LI Xiuzhi, HE Yalei, SUN Yanjun, ZHANG Xiangyin, ZHANG Xiaofan. Autonomous Exploration of Mobile Robot Based on Compound Cooperative Strategy[J]. ROBOT, 2021, 43(1): 44-53. DOI: 10.13973/j.cnki.robot.200009
Citation: LI Xiuzhi, HE Yalei, SUN Yanjun, ZHANG Xiangyin, ZHANG Xiaofan. Autonomous Exploration of Mobile Robot Based on Compound Cooperative Strategy[J]. ROBOT, 2021, 43(1): 44-53. DOI: 10.13973/j.cnki.robot.200009

基于复合式协同策略的移动机器人自主探索

Autonomous Exploration of Mobile Robot Based on Compound Cooperative Strategy

  • 摘要: 为了有效地解决机器人在空旷的厅堂环境下的探索难题以及RRT (快速扩展随机树)难以在含有狭窄入口的环境下快速扩展的问题,提出了一种将RRT与前沿法协同实施的复合式候选目标点检测策略;此外,提出一种有效的代价值计算方法,以代价值作为最优候选目标点的评价准则;并且设计了改进的TEB (时间弹性带)算法以实现机器人的局部路径规划,确保机器人顺利到达目标点.在同样的实验条件下,在实际环境下所提方法的探索时间、行驶距离、探索次数3个参数分别为1187.465 s、97.551 m、41,在仿真环境下分别为275.119 s、130.051 m、32,较GTM (栅格-拓扑地图)、RRT的探索性能均有所提升.结果表明该方法有效地解决了机器人在空旷的厅堂环境下的探索难题以及RRT难以在含有狭窄入口的环境下快速扩展的问题.

     

    Abstract: It is difficult for a robot to explore an empty hall environment, and the RRT (rapidly-exploring random tree) is difficult to expand rapidly in the environment with narrow entrances. In order to effectively solve these problems, a compound detection strategy of candidate target points is proposed by combining RRT and the frontier method. In addition, the cost value is taken as the evaluation criterion of the optimal candidate target point, and an effective cost calculation method is proposed. Furthermore, an improved TEB (timed elastic band) algorithm is designed to realize local path planning of the robot and ensure the robot to reach the target point smoothly. In the actual environment, three parameters of the proposed method, the exploration time, the driving distance and the exploration times, are 1 187.465 s, 97.551 m and 41, respectively; while in the simulation environment, they are 275.119 s, 130.051 m and 32, respectively, which are better than GTM (gridtopological map) and RRT methods under the same experimental conditions. Results show that the method can effectively solve both the problem of robot exploration in the empty hall environment, and the problem of RRT rapid expansion in the environment with narrow entrances.

     

/

返回文章
返回