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).
翻译:无人驾驶航空飞行器(无人驾驶飞行器)由于可自主使用的广泛应用而一直站出来,但它们需要智能系统,能够使人们更好地了解他们认为可以执行的几项任务,在复杂的环境中,这些飞行器变得更加具有挑战性,因为需要对环境进行感知,并在环境不确定的情况下采取行动,以便作出决定。在这方面,一个使用积极观念的系统可以通过在迁移发生时确认目标来寻求最佳的下一个观点来提高性能。这项工作旨在通过解决跟踪和识别水表结构以进行动态着陆的问题,帮助积极认识无人驾驶飞行器。我们显示,我们具有传统图像处理技术和简单的深层强化学习(深层强化学习(深层学习)代理的系统有能力在不使用复杂的革命神经网络(CNN)或对抗性学习(CL)的情况下,了解环境和处理不确定性。