An Optimization Framework for Multirobot Space Exploration With Hybrid RRT-FE and Market Mechanisms

In this article, a novel space exploration method based on rapidly exploring random tree (RRT) and frontier-based exploration is proposed to efficiently build a map utilizing multirobot in an unknown environment. A hybrid RRT and frontier-based detector is designed to find the target point. Specifically, the RRT algorithm is used to detect frontier points; then, the frontier points are further expanded to obtain the continuous frontier region by a frontier-based exploration method, and the centroid of the continuous frontier region is selected as the exploration target point. In addition, a task assignment strategy based on improved market mechanism is adopted to allocate the detected target points more effectively by incorporating the state of robot into profit function. A series of experiments is carried out to verify the proposed method and the experimental results show that the proposed method can reduce the time cost and exploration trajectory length in both simulations and prototype environments.