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Research on multi-UAV task allocation for power inspection based on multistrategy improved particle swarm algorithm

Year: 2025

Type: conference-paper

Abstract: The UAV (Unmanned Aerial Vehicle) task allocation problem in power inspection is a complex optimization problem aimed at improving inspection efficiency, reducing costs, and ensuring the safety and reliability of power facilities.In this paper, a method based on the Improved Particle Swarm Optimization (IPSO) algorithm is proposed to solve the task allocation problem of UAV swarm in power inspection.First, the distribution map of power lines is constructed by obtaining the actual power lines and tower location information, and the lines are partitioned into several task segments according to the tower locations.Then, the improved particle swarm algorithm is applied to optimize the task allocation strategy of the UAV population, considering the task cost and task balance.To overcome the shortcomings of the traditional particle swarm algorithm, this paper introduces Piecewise initialization, dynamic weight updating, and Levi's flight strategy, which improves the algorithm's global search ability and convergence speed.The experimental results indicate that the improved particle swarm algorithm significantly decreases both the overall cost of the task and the individual cost of each UAV, improves the task balance, and substantially optimizes the task allocation, which proves the applicability and superiority of the proposed method in applications.

Source: International Conference on Computer Application and Information Security (ICCAIS)

DOI: 10.1117/12.3060609

Authors: Min Luo, Maowei Zhou

Institution: Southwest Petroleum University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W4409784743