Work

Autonomous Task Planning of Intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient

Year: 2025

Type: article

Abstract: Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence.

Source: Drones

DOI: 10.3390/drones9040272

Authors: Qiang Jiang, Yongzhao Yan, Yinxing Dai, Zequan Yang, Huazhen Cao, Bo Wang, Xiaoping Ma

Institutions: Chinese Academy of Sciences, Institute of Engineering Thermophysics, University of Chinese Academy of Sciences, Central South University, National Supercomputing Changsha Center

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

OpenAlex ID: W4409141207

FWCI: 1.224

Cites: 34

Cited by: 3

Related to: 10