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This paper addresses the challenge of autonomous task planning for UAV swarms in SEAD missions within A2/AD environments by developing a six-tier simulation environment and employing a Deep Deterministic Policy Gradient (DDPG) algorithm. The authors designed specific reward functions to encourage effective action strategies within the UAV swarm. Simulation results demonstrate the feasibility of the proposed environment and the ability of the DDPG-trained UAV swarm to exhibit enhanced autonomous task planning capabilities.
DDPG, enhanced with custom reward functions, enables UAV swarms to autonomously plan and execute complex SEAD missions in simulated A2/AD environments.
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.
