Mobile Robot Exploration Strategy Based on Multiple Information Gain

Aiming at solving the problem of blindness in autonomous exploration and mapping by mobile robots in unknown environments, an exploration strategy based on Bayesian optimization to evaluate multiple information gains was proposed. In the candidate point extraction method, the method of integrating frontier point clustering and passable area to comprehensively measure and extract was adopted. Compared with the traditional frontier point detection method, it effectively solved the problems of excessive candidate point sets and missing environmental information. The Bayesian optimization was used to calculate multiple information gains considering both map entropy and distance costs. Compared with the method of selecting the best candidate point based solely on map entropy, this method effectively improved the redundancy path of the robot in the environment. Gazebo was used to verify the algorithm in robot operating system (ROS) and build environment map. Results show that the proposed method can enable the mobile robot to explore the unknown environment quickly and efficiently and complete the mapping task with high quality.