基于拓扑地图的移动机器人室内环境高效自主探索算法

An Efficient Autonomous Exploration Algorithm of Indoor Environment for Mobile Robots Using Topological Map

为了减少移动机器人在自主探索过程中反复到达已知区域的次数,从而提高自主探索效率,提出一种高效率自主探索算法TMRRT(topological map based rapidly exploring random tree)。首先,将变生长率的局部与全局快速扩展随机树(RRT)作为探测器来发现地图的边界,并对前沿点进行聚类;同时,将最佳探测点存储下来作为拓扑地图,避免机器人反复到达已探索区域。最后,在不同环境下进行仿真并在实际环境中进行验证。实验结果显示,本文的探索算法相对于RRT算法平均探索时长减小了7.5%以上、平均路径长度减小了19.8%以上,相对于FA(frontier-based approach)自主探索算法平均探索时长减小了15.7%以上、平均路径长度减小了34.3%以上。结果表明,该算法可以有效提高机器人自主探索的效率,在实际环境中具有可行性。

An efficient autonomous exploration algorithm TMRRT(topological map based rapidly exploring random tree)is proposed to reduce the times of reaching a known area repeatedly by the mobile robot in the process of autonomous exploration,and thus to improve the efficiency of autonomous exploration.Firstly,the local and global RRTs(rapidly exploring random trees)with variable growth rate are used as the detector to find the map boundary,and cluster the frontier points.Meanwhile,the best detection points are stored as a topological map to avoid reaching the explored area repeatedly by the robot.Finally,the simulations are carried out in different environments and the experiments are carried out in an actual environment.The experimental results show that the average exploration time can be reduced by more than 7.5%and the average path length can be reduced by more than 19.8%by the the proposed exploration algorithm compared with RRT autonomous exploration algorithm,and the average exploration time can be reduced by more than 15.7%and the average path length can be reduced by more than 34.3%compared with FA(frontier-based approach)autonomous exploration algorithm.The results show that the algorithm can effectively improve the efficiency of robot autonomous exploration and is feasible in the actual environment.