An Efficient Autonomous Exploration Method based on Frontier and Space Division for Quadrotors

The one of essential requirements for autonomous exploration is efficiency, but the performance of existing methods is not satisfactory for unreasonable paths, especially in complex, cluttered, and large-scale environments. To improve the efficiency of autonomous exploration, this paper proposes an efficient method that considers the coverage path to get the next division by dividing the entire exploration space into several uniform divisions, and the quadrotors will take the corresponding corner division from the next division as the target region for exploration path planning to get the best next viewpoint until there is no frontier cluster in the environment. The extensive simulation experiments demonstrate that the proposed method improves exploration efficiency by 5%-15% compared to three classic and mainstream methods in most scenarios.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/icaisisas64483.2025.11051866
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
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article

An Efficient Autonomous Exploration Method based on Frontier and Space Division for Quadrotors

Zhaoqi Wang, Kang Hu, Haoxiang Chen, Jinting Liu
Robotic Path Planning Algorithms
article

An Efficient Autonomous Exploration Method based on Frontier and Space Division for Quadrotors

Zhaoqi Wang, Kang Hu, Haoxiang Chen, Jinting Liu
article en

Abstract

The one of essential requirements for autonomous exploration is efficiency, but the performance of existing methods is not satisfactory for unreasonable paths, especially in complex, cluttered, and large-scale environments. To improve the efficiency of autonomous exploration, this paper proposes an efficient method that considers the coverage path to get the next division by dividing the entire exploration space into several uniform divisions, and the quadrotors will take the corresponding corner division from the next division as the target region for exploration path planning to get the best next viewpoint until there is no frontier cluster in the environment. The extensive simulation experiments demonstrate that the proposed method improves exploration efficiency by 5%-15% compared to three classic and mainstream methods in most scenarios.

Beijing Institute of Technology (CN)
Openalex Percentile: Top 86%
Robotic Path Planning Algorithms
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