Indoor 2D Autonomous Exploration with an Omnidirectional Robot: A Strategy Based on Rapidly-Exploring Random Trees

Abstract

An essential characteristic of a fully autonomous robot is the capability to examine an unfamiliar environment and construct a representation of it. The challenge of autonomous exploration involves overcoming various sub-problems, including Simultaneous Localization and Mapping (SLAM), motion planning, target identification, and informed decision-making for target selection. This paper presents a frontier-based methodology to identify potential navigation targets for the autonomous exploration of unknown environments by an omnidirectional robot. Permanent and temporary Rapidly-exploring Random Tree (RRT)-based structures are used to search the map and detect frontier points. A novel temporary RRT-based structure, Frontier Temporary Tree, is introduced in this study. It is noteworthy that RRT is solely used to search the explorable space for frontier points and does not contribute to motion planning. A cost-benefit analysis, taking into account path cost, heading cost, and information gain, is used to evaluate the frontier points and determine the best target among them. The proposed method is subjected to rigorous testing through both simulation and experimental studies with an omnidirectional robot under real-world scenarios. Comparative results from simulation studies show that our method consistently outperforms, demonstrating its robustness and efficacy.