Fast autonomous exploration with sparse topological graphs in large-scale environments

Wei, Changyun, Wu, Jianbin, Xia, Yu and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2024. Fast autonomous exploration with sparse topological graphs in large-scale environments. International Journal of Intelligent Robotics and Applications 8 , pp. 111-121. 10.1007/s41315-023-00318-7

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Abstract

Exploring large-scale environments autonomously poses a significant challenge. As the size of environments increases, the computational cost becomes a hindrance to real-time operation. Additionally, while frontier-based exploration planning provides convenient access to environment frontiers, it suffers from slow global exploration speed. On the other hand, sampling-based methods can effectively explore individual regions but fail to cover the entire environment. To overcome these limitations, we present a hierarchical exploration approach that integrates frontier-based and sampling-based methods. It assesses the informational gain of sampling points by considering the quantity of frontiers in the vicinity, and effectively enhances exploration efficiency by utilizing a utility function that takes account of the direction of advancement for the purpose of selecting targets. To improve the search speed of global topological graph in large-scale environments, this paper introduces a method for constructing a sparse topological graph. It incrementally constructs a three-dimensional sparse topological graph by dynamically capturing the spatial structure of free space through uniform sampling. In various challenging simulated environments, the proposed approach demonstrates comparable exploration performance in comparison with the state-of-the-art approaches. Notably, in terms of computational efficiency, the single iteration time of our approach is less than one-tenth of that required by the recent advances in autonomous exploration.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Springer
ISSN:	2366-5971
Date of First Compliant Deposit:	6 February 2024
Date of Acceptance:	30 December 2023
Last Modified:	08 Apr 2024 13:48
URI:	https://orca.cardiff.ac.uk/id/eprint/166153

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