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Complex-Scene-Oriented Autonomous Decision-Making Method for UAVs
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DOI：10.3390/electronics15081757.png)
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Abstract
En 中文
The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of 8 ± 1.2 , which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios.
Keywords:
deep reinforcement learning
unmanned aerial vehicle
autonomous decision-making
complex scenes
curiosity mechanism
contrastive learning
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