Cooperative Task Assignment For Multi-UAV Attack Mobile Targets

Chen, Xia; Liu, Yongtai

doi:10.1109/cac48633.2019.8996383

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…The cooperative multiple assignment problem is a computationally intractable problem. In order to implement the optimisation process, the researchers approximated this problem as a graph based on the heading angle discretization [33][34][35] .…”

Section: Modeling the Uav Tasking Problem Based On Different Performa...mentioning

confidence: 99%

“…where . Edison and Shima [35] and Deng et al [33] chose to minimize the cumulative distance flown by all UAVs and minimize the execution time required for the UAVs to complete the task as the objective functions, respectively. Since the UAVs fly at a constant speed, these two types of objective functions are equivalent.…”

Section: Modeling the Uav Tasking Problem Based On Different Performa...mentioning

confidence: 99%

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Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Li,

Shao

et al. 2024

CAAI Artificial Intelligence Research

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Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.

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Section: Modeling the Uav Tasking Problem Based On Different Performa...mentioning

confidence: 99%

Section: Modeling the Uav Tasking Problem Based On Different Performa...mentioning

confidence: 99%

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Li,

Shao

et al. 2024

CAAI Artificial Intelligence Research

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show abstract

“…Xiang Ma et al proposed an improved virtual force method to obtain the optimal damage effectiveness control strategy, which solves the numerical solution problem caused by the high complexity of the damage function [30]. Xia Chen et al proposed a systematic framework for solving the combinatorial optimization model with a new particle swarm optimization algorithm based on a bootstrapping mechanism, and for the trajectory planning problem, an ant colony optimization algorithm based on adaptive parameter tuning and bidirectional search was proposed [31]. Taehoon Yoo et al proposed a reinforcement learning-based intelligent formation controller that enables each UAV to communicate with other UAVs [32].…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Liang,

Li,

2023

Sensors

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To address the challenge of coordinated combat involving multiple UAVs in reconnaissance and search attacks, we propose the Multi-UAV Distributed Self-Organizing Cooperative Intelligence Surveillance and Combat (CISCS) strategy. This strategy employs distributed control to overcome issues associated with centralized control and communication difficulties. Additionally, it introduces a time-constrained formation controller to address the problem of unstable multi-UAV formations and lengthy formation times. Furthermore, a multi-task allocation algorithm is designed to tackle the issue of allocating multiple tasks to individual UAVs, enabling autonomous decision-making at the local level. The distributed self-organized multi-UAV cooperative reconnaissance and combat strategy consists of three main components. Firstly, a multi-UAV finite time formation controller allows for the rapid formation of a mission-specific formation in a finite period. Secondly, a multi-task goal assignment module generates a task sequence for each UAV, utilizing an improved distributed Ant Colony Optimization (ACO) algorithm based on Q-Learning. This module also incorporates a colony disorientation strategy to expand the search range and a search transition strategy to prevent premature convergence of the algorithm. Lastly, a UAV obstacle avoidance module considers internal collisions and provides real-time obstacle avoidance paths for multiple UAVs. In the first part, we propose a formation algorithm in finite time to enable the quick formation of multiple UAVs in a three-dimensional space. In the second part, an improved distributed ACO algorithm based on Q-Learning is introduced for task allocation and generation of task sequences. This module includes a colony disorientation strategy to expand the search range and a search transition strategy to avoid premature convergence. In the third part, a multi-task target assignment module is presented to generate task sequences for each UAV, considering internal collisions. This module provides real-time obstacle avoidance paths for multiple UAVs, preventing premature convergence of the algorithm. Finally, we verify the practicality and reliability of the strategy through simulations.

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“…At present, scholars have conducted a large body of research on task allocation and have achieved a series of results. The related research works mainly use intelligent algorithms (ant colony algorithms [12][13][14][15], genetic algorithms [16][17][18], particle swarm algorithms [19][20][21][22], artificial neural networks [23], etc. ), and contract network algorithms [24][25][26] to solve task coordination problems in a distributed architecture.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

et al. 2022

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With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.

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Cooperative Task Assignment For Multi-UAV Attack Mobile Targets

Cited by 10 publications

References 5 publications

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Multi-UAV Collaborative Search and Attack Mission Decision-Making in Unknown Environments

Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

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