A Modeling Method of Multiple Targets Assignment under Multiple UAVs’ Cooperation

Wang, Q H; Guo-Bin, Wan; Cao, Xiangyang; Xie, Liming

doi:10.1088/1757-899x/187/1/012006

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Recent developments in unmanned aerial vehicles (UAVs) have made them indispensable tools in modern society. They can perform a variety of tasks previously done by humans in dangerous and complex environments, such as target searching, attacking, and so on [1]. For complicated tasks, cooperative formations of UAVs have become an important means of improving efficiency.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

Qie

Shi²,

Shen³

et al. 2019

IEEE Access

207

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One of the major research topics in unmanned aerial vehicle (UAV) collaborative control systems is the problem of multi-UAV target assignment and path planning (MUTAPP). It is a complicated optimization problem in which target assignment and path planning are solved separately. However, recalculation of the optimal results is too slow for real-time operations in dynamic environments because of the large number of calculations required. In this paper, we propose an artificial intelligence method named simultaneous target assignment and path planning (STAPP) based on a multi-agent deep deterministic policy gradient (MADDPG) algorithm, which is a type of multi-agent reinforcement learning algorithm. In STAPP, the MUTAPP problem is first constructed as a multi-agent system. Then, the MADDPG framework is used to train the system to solve target assignment and path planning simultaneously according to a corresponding reward structure. The proposed system can deal with dynamic environments effectively as its execution only requires the locations of the UAVs, targets, and threat areas. Real-time performance can be guaranteed as the neural network used in the system is simple. In addition, we develop a technique to improve the training effect and use experiments to demonstrate the effectiveness of our method.INDEX TERMS Multi-UAV, target assignment and path planning, multi-agent reinforcement learning, MADDPG, dynamic environments.

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Section: Introductionmentioning

confidence: 99%

Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

Qie

Shi²,

Shen³

et al. 2019

IEEE Access

207

View full text Add to dashboard Cite

show abstract

Cooperative task assignment for UAV based on SA-QCDPSO

Zhang

Chen

Tang

et al. 2020

2020 IEEE 16th International Conference on Control &Amp; Automation (ICCA)

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Formation Cooperative Intelligent Tactical Decision Making Based on Bayesian Network Model

Guo,

Zhang,

Wang

et al. 2024

Drones

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This paper proposes a method based on a Bayesian network model to study the intelligent tactical decision making of formation coordination. For the problem of formation coordinated attack target allocation, a coordinated attack target allocation model based on the dominance matrix is constructed, and a threat degree assessment model is constructed by calculating the minimum interception time. For the problem of real-time updating of the battlefield situation in the formation confrontation simulation, real-time communication between the UAV formation on the battlefield is realized, improving the efficiency of communication and target allocation between formations on the battlefield. For the problem of UAV autonomous air combat decision making, on the basis of the analysis of the advantage function calculation of the air combat decision-making model and a Bayesian network model analysis, the network model’s nodes and states are determined, and the air combat decision-making model is constructed based on the Bayesian network. Our formation adopts the Bayesian algorithm strategy to fight against the blue side’s UAVs, and the formation defeats the blue UAVs through coordinated attack, which proves the reasonableness of coordinated target allocation. An evaluation function is established, and the comprehensive scores of our formation are compared with those of other algorithms, which proves the accuracy and intelligibility of the decision making of the Bayesian network.

show abstract

A Modeling Method of Multiple Targets Assignment under Multiple UAVs’ Cooperation

Cited by 3 publications

References 6 publications

Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

Cooperative task assignment for UAV based on SA-QCDPSO

Formation Cooperative Intelligent Tactical Decision Making Based on Bayesian Network Model

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