Autonomous Exploration of Unknown Environments Using a Mobile Robot

This paper studies the problem of autonomous exploration in unknown environments with a mobile robot platform. By integrating a simultaneous localization and mapping system, the detected boundary points are filtered and used as navigation target points to accomplish autonomous exploration. A novel algorithm for boundary point detection, filtering, and target point assignment is proposed. For boundary point detection, due to the non-completeness of sensor-based random tree methods and the low efficiency of traditional rapidly-exploring random tree methods, this paper employs the probabilistically complete dualthread rapidly-exploring random trees algorithm. The dual-thread rapidly-exploring random trees algorithm adopts a dual-thread architecture that combines global and local trees to improve exploration efficiency while ensuring full coverage of the unknown environment. For boundary point filtering, the K-means clustering algorithm is replaced with the mean shift algorithm, reducing dependency on initial inputs and enhancing algorithm robustness. Regarding target point assignment, a Bayesian decision-making approach based on multiple prior inputs is utilized, ensuring the efficiency of the exploration process. Physical experiments demonstrate that the proposed approach achieves significant performance in the studied scenario.