Fast Active Aerial Exploration for Traversable Path Finding of Ground Robots in Unknown Environments
Shiyong Zhang, Xuebo Zhang, Tianyi Li, Jing Yuan, Yongchun Fang
Abstract
This article proposes an autonomous aerial exploration framework for traversable path finding of ground robots in unknown environments to achieve a fast response in search and rescue (SAR) missions. Different from existing works, our method provides a task-oriented active exploration strategy that makes the aerial robot fully autonomous during the exploration and does not require human intervention. To identify the candidate regions which ground robots can go through, a fast incremental traversability estimation algorithm is presented, which can evaluate the traversability of the region of interest (ROI) efficiently. Based on the traversability map, we propose a highly efficient frontier detection algorithm that can guarantee both reachability and safety of the extracted frontiers. To reduce the total response time of the whole robot system, a new cost function considering both the time costs of the aerial and ground robots is designed that aims to avoid back-and-forth motion behavior during aerial exploration. The performance of our approach is evaluated in both simulation and real-world experiments. The results show that the proposed method outperforms existing methods in terms of traversability estimation and frontier detection time (about one order of magnitude faster) as well as the response time (at least 13% shorter on average).