Task Allocation Optimization for Multi-UAV Collaborative Power Inspection at Single Parking Point

This paper focuses on task allocation for multi-UAV collaborative power inspection at a single parking point, proposing an optimization method to minimize the total inspection time per parking. Key contributions include: 1) establishing a dynamic model for multi-rotor UAVs and a collaborative inspection model with clear assumptions; 2) developing a technical route covering task allocation, path planning, and conflict detection; 3) designing an immune algorithm-based parking point selection method and a UAV-operation vehicle collaborative inspection model; 4) implementing a genetic algorithm for sparse tower scenarios and a variable neighborhood search (VNS) algorithm for dense scenarios; 5) studying anti-collision path planning and optimizing takeoff/landing strategies. Case simulations verify the algorithms’ effectiveness in different scenarios, providing a practical UAV allocation scheme to enhance inspection efficiency.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3611643
Primary Topic
Smart Parking Systems Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Task Allocation Optimization for Multi-UAV Collaborative Power Inspection at Single Parking Point

Zhike Wen, Han Cai, Zhuang Liu, Ning Yang et al.
IEEE Access
Smart Parking Systems Research
article

Task Allocation Optimization for Multi-UAV Collaborative Power Inspection at Single Parking Point

Zhike Wen, Han Cai, Zhuang Liu, Ning Yang, Guiwei Shao, Liwei Zhou
article en

Abstract

This paper focuses on task allocation for multi-UAV collaborative power inspection at a single parking point, proposing an optimization method to minimize the total inspection time per parking. Key contributions include: 1) establishing a dynamic model for multi-rotor UAVs and a collaborative inspection model with clear assumptions; 2) developing a technical route covering task allocation, path planning, and conflict detection; 3) designing an immune algorithm-based parking point selection method and a UAV-operation vehicle collaborative inspection model; 4) implementing a genetic algorithm for sparse tower scenarios and a variable neighborhood search (VNS) algorithm for dense scenarios; 5) studying anti-collision path planning and optimizing takeoff/landing strategies. Case simulations verify the algorithms’ effectiveness in different scenarios, providing a practical UAV allocation scheme to enhance inspection efficiency.

IEEE AccessVol. 13
China Power Engineering Consulting Group (China) (CN), China National Electric Apparatus Research Institute (China) (CN)
Openalex Percentile: Top 73%
Smart Parking Systems Research
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