Integrated dynamic task allocation via event-triggered for tracking ground moving targets by UAVs in urban

IF 5.2 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Robotics and Autonomous Systems Pub Date : 2025-05-29 DOI:10.1016/j.robot.2025.105061

Chaofang Hu , Yuan Li , Ge Qu

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引用次数: 0

Abstract

For multiple unmanned aerial vehicles (UAVs), real-time task allocation is essential in cooperative and persistent tracking of multiple ground moving targets (GMTs) in urban. In this paper, an integrated dynamic task allocation method via event-triggered is proposed. Firstly, a time-varying zero–one integer programming (ZOIP) model is built for task allocation and the receding horizon framework is proposed for path planning such that a mixed integer nonlinear programming (MINLP) problem is formulated. Using the relaxed order of satisfactory degrees, a fuzzy satisfaction goal programming model with three objectives and priorities is designed as the time-varying reward in the integrated task allocation. Secondly, in order to reduce the computational load, an event-triggered mechanism with three triggering conditions is designed to conduct switch of integrated optimization and pure path planning. Thirdly, in view of the difficulty of solving the integrated task allocation MINLP model, a binary hybrid particle swarm optimization (BHPSO) algorithm is employed. Finally, simulations validate that the proposed method is effective and efficient.

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基于事件触发的城市无人机跟踪地面运动目标集成动态任务分配

对于多架无人机来说，实时任务分配是实现城市多地面运动目标协同持久跟踪的关键。提出了一种基于事件触发的集成动态任务分配方法。首先，建立了任务分配的时变0 - 1整数规划（ZOIP）模型，提出了路径规划的后退地平线框架，从而形成了混合整数非线性规划（MINLP）问题。利用放宽的满意度顺序，设计了一个具有三个目标和优先级的模糊满意度目标规划模型，作为综合任务分配中的时变奖励。其次，为了减少计算量，设计了具有三种触发条件的事件触发机制，实现了综合优化与纯路径规划的切换。第三，针对集成任务分配MINLP模型求解困难的问题，采用了二元混合粒子群优化算法（BHPSO）。最后通过仿真验证了该方法的有效性和高效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Autonomous Systems 工程技术-机器人学

CiteScore

9.00

自引率

7.00%

发文量

164

审稿时长

4.5 months

期刊介绍： Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.