Issue |
Int. J. Metrol. Qual. Eng.
Volume 15, 2024
|
|
---|---|---|
Article Number | 18 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/ijmqe/2024017 | |
Published online | 04 October 2024 |
Research Article
Path planning of quadruped robot for urban natural gas pipe leakage inspection based on optimized RRT* and DWA algorithms
1
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, 310018, China
2
College of Energy Environment and Safety Engineering, China Jiliang University, Hangzhou, 310018, China
3
College of Quality and Standardization, China Jiliang University, Hangzhou, 310018, China
4
Lanxi Xinao Gas Co. Ltd, Jinhua, 321000, China
* Corresponding author: qiangwang@cjlu.edu.cn
Received:
20
June
2024
Accepted:
27
August
2024
The leakage of urban natural gas pipes may cause significant safety hazards and economic losses. Autonomous inspection of these pipes using quadruped robots is an effective inspection method. This paper proposes a hybrid algorithm combining optimized RRT* and DWA(ORRT*-DWA) to solve the path planning problem faced by quadruped robots in urban environment. Firstly, the RRT* algorithm is optimized through three strategies, including probability-based sampling, extended node filtering, and adaptive step size. The ORRT* algorithm is then integrated with the DWA algorithm to form the new path planning algorithm. The ORRT*-DWA algorithm achieves higher efficiency in path optimization and enables local dynamic obstacle avoidance. Then, the performance of ORRT*-DWA algorithm is compared with RRT* algorithm and the informed RRT* algorithm. Results show that the global planning path length is reduced by 8.9% and the actual path length by 4.2%. Finally, a field test conducted in a 100 m × 50 m urban residential area shows that the ORRT*-DWA algorithm plans shorter and smoother paths compared to the informed RRT* algorithm, achieving a 9.7% reduction in path length.
Key words: Natural gas pipe leakage inspection / quadruped robot / ORRT*-DWA / path planning
© Y. Wu et al., published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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