Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm

Zhou Zhenzhen; Song Yunhai; He Sen; Huang Heyan; He Yuhao; Li Weiming; Yan Yingjie

doi:10.1109/PSGEC58411.2023.10255888

Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm

Zhou Zhenzhen
, Song Yunhai
, He Sen
, Huang Heyan
, He Yuhao
, Li Weiming
, Yan Yingjie

CSG EHV Transmission Company
Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.

Original language	English
Title of host publication	Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023
Editors	Guojie Li, Zhigang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	879-883
Number of pages	5
ISBN (Electronic)	9798350340099
DOIs	https://doi.org/10.1109/PSGEC58411.2023.10255888
Publication status	Published - 2023
Externally published	Yes
Event	3rd Power System and Green Energy Conference, PSGEC 2023 - Shanghai, China Duration: 24 Aug 2023 → 26 Aug 2023

Publication series

Name	Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023

Conference

Conference	3rd Power System and Green Energy Conference, PSGEC 2023
Country/Territory	China
City	Shanghai
Period	24/08/23 → 26/08/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

inspection points
intelligent equipment
path planning
shooting quality
the improved Ant-Lion Optimization Algorithm

Access to Document

10.1109/PSGEC58411.2023.10255888

Cite this

Zhenzhen, Z., Yunhai, S., Sen, H., Heyan, H., Yuhao, H., Weiming, L., & Yingjie, Y. (2023). Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm. In G. Li, & Z. Li (Eds.), Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023 (pp. 879-883). (Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PSGEC58411.2023.10255888

Zhenzhen, Zhou ; Yunhai, Song ; Sen, He et al. / Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm. Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023. editor / Guojie Li ; Zhigang Li. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 879-883 (Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023).

@inproceedings{daf6267a56824ed898895bec9b1bb390,

title = "Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm",

abstract = "The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.",

keywords = "inspection points, intelligent equipment, path planning, shooting quality, the improved Ant-Lion Optimization Algorithm",

author = "Zhou Zhenzhen and Song Yunhai and He Sen and Huang Heyan and He Yuhao and Li Weiming and Yan Yingjie",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd Power System and Green Energy Conference, PSGEC 2023 ; Conference date: 24-08-2023 Through 26-08-2023",

year = "2023",

doi = "10.1109/PSGEC58411.2023.10255888",

language = "English",

series = "Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "879--883",

editor = "Guojie Li and Zhigang Li",

booktitle = "Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023",

address = "United States",

}

Zhenzhen, Z, Yunhai, S, Sen, H, Heyan, H, Yuhao, H, Weiming, L & Yingjie, Y 2023, Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm. in G Li & Z Li (eds), Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023. Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 879-883, 3rd Power System and Green Energy Conference, PSGEC 2023, Shanghai, China, 24/08/23. https://doi.org/10.1109/PSGEC58411.2023.10255888

Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm. / Zhenzhen, Zhou; Yunhai, Song; Sen, He et al.
Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023. ed. / Guojie Li; Zhigang Li. Institute of Electrical and Electronics Engineers Inc., 2023. p. 879-883 (Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm

AU - Zhenzhen, Zhou

AU - Yunhai, Song

AU - Sen, He

AU - Heyan, Huang

AU - Yuhao, He

AU - Weiming, Li

AU - Yingjie, Yan

PY - 2023

Y1 - 2023

N2 - The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.

AB - The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.

KW - inspection points

KW - intelligent equipment

KW - path planning

KW - shooting quality

KW - the improved Ant-Lion Optimization Algorithm

UR - https://www.scopus.com/pages/publications/85174256445

U2 - 10.1109/PSGEC58411.2023.10255888

DO - 10.1109/PSGEC58411.2023.10255888

M3 - Conference contribution

AN - SCOPUS:85174256445

T3 - Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023

SP - 879

EP - 883

BT - Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023

A2 - Li, Guojie

A2 - Li, Zhigang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd Power System and Green Energy Conference, PSGEC 2023

Y2 - 24 August 2023 through 26 August 2023

ER -

Zhenzhen Z, Yunhai S, Sen H, Heyan H, Yuhao H, Weiming L et al. Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm. In Li G, Li Z, editors, Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 879-883. (Proceedings - 2023 3rd Power System and Green Energy Conference, PSGEC 2023). doi: 10.1109/PSGEC58411.2023.10255888

Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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