CURE: A Hierarchical Framework for Multi-Robot Autonomous Exploration Inspired by Centroids of Unknown Regions

In this paper, a novel multi-robot autonomous exploration approach CURE is proposed based on dynamic Voronoi diagrams and centroids of unknown connected regions. Compared with existing approaches, the novelty of this work is twofold: 1) Dynamic Voronoi diagram is used for partition of the space being explored to improve the efficiency of multi-robot exploration, and then a new parameter-insensitive utility function is elaborately designed to evaluate the information of centroids, which helps guide the robot to explore unknown regions. 2) A hierarchical framework consisting of global and local exploration windows for detecting centroids is designed, wherein the global exploration window is activated to find centroids to guide the robot exploration when there are no centroids in any one local exploration window. We validate the feasibility and exploration efficiency of the proposed approach in various complex simulation scenarios and challenging real-world tasks. All test results show that the exploration time consumption and path cost are reduced by up to 50.7% and 34.4%, respectively, compared with an advanced RRT-based multi-robot exploration approach. (Supplementary video link: https://youtu.be/P5jXKlGQOec)
Note to Practitioners
—This paper is motivated by the efficient multi-robot autonomous exploration problem. In some applications such as target search and disaster rescue, the information about the environment is totally unknown to the robots, and thus they are required to explore unknown environments autonomously. In this case, it is necessary to improve the efficiency of multi-robot exploration due to the time limitation of the task and the battery capacity. In this paper, a hierarchical framework is proposed to improve the efficiency of multi-robot autonomous exploration. Each robot only needs to explore the Voronoi partition it is responsible for and is guided to the unknown region by the centroid detected in the global and local exploration windows. Overall, the proposed approach can dramatically reduce the exploration time and path cost.