UFOExplorer: Fast and Scalable Sampling-Based Exploration With a Graph-Based Planning Structure

摘要

We propose UFOExplorer, a fast and efficient exploration method that scales well with the environment size. An exploration paradigm driven by map updates is proposed to enable the robot to react quicker and to always move towards the optimal exploration goal. For each map update, a dense graph-based planning structure is updated and extended. The planning structure is then used to generate a path using a simple exploration heuristic, which guides the robot towards the closest exploration goal. The proposed method scales well with the environment size, as the planning cost is amortized when updating and extending the planning structure. The simple exploration heuristic performs on par with the most recent state-of-the-art methods in smaller environments and outperforms them in larger environments, both in terms of exploration speed and computational efficiency. The implementation of the method is made available for future research.