论文标题
通过深厚的增强学习应用于无人飞机的积极感知
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning
论文作者
论文摘要
由于可以自主使用的广泛应用，无人驾驶汽车（UAV）一直脱颖而出。但是，他们需要能够对执行多个任务的看法有更深入的了解。在复杂的环境中，它们变得更具挑战性，因为有必要感知环境并在环境不确定性下采取行动以做出决定。在这种情况下，使用主动感知的系统可以通过在发生位移时识别目标来寻求最佳下一个观点，从而提高性能。这项工作旨在通过解决跟踪和识别水面结构以执行动态着陆的问题来为无人机的积极感知做出贡献。我们表明，使用经典图像处理技术的系统和简单的深钢筋学习（DEEP-RL）代理能够感知环境并处理不确定性，而无需使用复杂的卷积神经网络（CNN）或对比度学习（CL）。
Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable of providing a greater understanding of what they perceive to perform several tasks. They become more challenging in complex environments since there is a need to perceive the environment and act under environmental uncertainties to make a decision. In this context, a system that uses active perception can improve performance by seeking the best next view through the recognition of targets while displacement occurs. This work aims to contribute to the active perception of UAVs by tackling the problem of tracking and recognizing water surface structures to perform a dynamic landing. We show that our system with classical image processing techniques and a simple Deep Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment and dealing with uncertainties without making the use of complex Convolutional Neural Networks (CNN) or Contrastive Learning (CL).
