A Hierarchical Autonomous Exploration Algorithm for Large-scale and Complex Environments with Mobile Robot
Qian Chen, Lizhe Qi, Zhongwei Hua, Zhi Yang, Yunquan Sun · 2023
In order to solve the problem of low exploration efficiency caused by repeatedly backtracking the frontiers in large and complex environments, a hierarchical autonomous exploration algorithm is proposed. Firstly, based on the robot's static model to judge the environment's traversability, which allows robots to explore complex 3D environments. Following that, a hybrid strategy is used to filter out frontiers within a local planning horizon, thus complete exploration of the local area is achieved by solving the traveling salesman problem (TSP). Finally, the sparse global topology map generates transfer paths between sub-areas, transferring the robot to another sub-area to resume exploration. Compared to the RRT autonomous exploration algorithm and the GBP autonomous exploration algorithm, the method in this work reduces the exploration path by more than 13.8% and the exploration time by more than 23.7%. The results show that the proposed algorithm significantly improves the autonomous exploration efficiency of mobile robots in large and complex environments.