SET: Sampling-Enhanced Exploration Tree for Mobile Robot in Restricted Environments

Abstract

Mobile robots generally work in harsh and restricted environments, which poses challenges for mobile robots to find a feasible path efficiently. This article presents a planning method, namely sampling-enhanced exploration tree (SET), to improve computational efficiency in restricted environments while guaranteeing high-quality performance. The core of SET is sampling-enhanced exploration, which consists of critical areas identification, guiding-exploration, and rectifying-exploration. In the critical areas identification phase, the restricted areas are identified based on the distribution of the hybrid samples. Next, the critical samples in restricted areas are selected as the origins of the sampling-enhanced exploration. In the guiding-exploration phase, the sampling-enhanced exploration starts from the origins and marches quickly with the guidance of the leader-samples to capture the spatial feature and connectivity of the restricted areas. The spatial information provides essential guidance for efficient biased sampling. In the rectifying-exploration phase, the directions of sampling-enhanced exploration are rectified to transit the problematic areas and supplement samples. Theoretical analysis is provided to shed light on the properties of SET. Moreover, the generality and effectiveness of SET are verified through a series of mobile robot simulations and real-world experiments.