LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains

Ground robot autonomous exploration for uneven terrains is still challenging since the rugged terrain structures not only degrade the exploration performance but also threaten the navigation safety of the robot. In this letter, a novel exploration planner is proposed for safe and fast exploration in uneven terrains. To obtain high exploration efficiency, we propose a large-region-aware exploration route optimization strategy that prioritizes exploring large regions while also considering exploring nearby small regions. To safely and completely explore uneven terrains, our planner fully introduces traversability information to extract unknown regions and assess exploration safety levels. The safety levels are then integrated into the design of the exploration strategy to ensure safe robotic exploration. We validate our method in various challenging simulation scenes and real-world wild uneven terrains. The results show that our method can safely explore uneven terrains and improve exploration efficiency by up to 45.3% compared with state-of-the-art methods.

Authors

Institutions

Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-10-24
DOI
https://doi.org/10.1109/lra.2024.3486229
Citations
10
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
2.49

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains

Qingchen Bi, Lun Li, Xuebo Zhang, Jing Yuan et al.
10 citations
IEEE Robotics and Automation Letters
Robotic Path Planning Algorithms
2.49
article

LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains

Qingchen Bi, Lun Li, Xuebo Zhang, Jing Yuan, Shiyong Zhang, Runhua Wang
article en
10 citations

Abstract

Ground robot autonomous exploration for uneven terrains is still challenging since the rugged terrain structures not only degrade the exploration performance but also threaten the navigation safety of the robot. In this letter, a novel exploration planner is proposed for safe and fast exploration in uneven terrains. To obtain high exploration efficiency, we propose a large-region-aware exploration route optimization strategy that prioritizes exploring large regions while also considering exploring nearby small regions. To safely and completely explore uneven terrains, our planner fully introduces traversability information to extract unknown regions and assess exploration safety levels. The safety levels are then integrated into the design of the exploration strategy to ensure safe robotic exploration. We validate our method in various challenging simulation scenes and real-world wild uneven terrains. The results show that our method can safely explore uneven terrains and improve exploration efficiency by up to 45.3% compared with state-of-the-art methods.

IEEE Robotics and Automation LettersVol. 9(12)
Nankai University (CN), Guangzhou University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Tianjin City, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 9%
Robotic Path Planning Algorithms
2.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.