A Cooperative Robot Exploration Strategy based on Frontier Methods and Sensor-Based Random Trees

Abstract

This paper presents a cooperative robot exploration (CRE) strategy that is based on the sensor-based random tree (SRT) star method. The CRE strategy is utilized for a team of mobile robots equipped with range finding sensors. Existing backtracking techniques for frontier-based (FB) exploration involve moving back thorough the previous position where the robot has passed before. However, in some cases, the robot generates inefficient detours to move back to the position that contains frontier areas. In an effort to improve upon movement and energy efficiencies, this paper proposes the use of a hub node that has a frontier arc; thereby, the robots backtrack more directly to hub nodes by using the objective function. Furthermore, each robot cooperatively explores the workspace utilizing the data structure from the entire team of robots, which consists of configuration data and frontier data. Comparative simulations of the proposed algorithm and the existing SRT-star algorithm are implemented and described. The experiment is presented to demonstrate the application of the proposed strategy in real-time. Utilizing the proposed algorithm and exploration strategy, the results indicate that a team of robots can work more efficiently by reducing the distance of exploration and the number of node visited.