Abstract-Unmanned Aerial Vehicles are currently investigated as an important sub-domain of robotics, a fast growing and truly multidisciplinary research field. UAVs are increasingly deployed in real-world settings for missions in dangerous environments or in environments which are challenging to access. Combined with autonomous flying capabilities, many new possibilities, but also challenges, open up. To overcome the challenge of early identification of degradation, machine learning based on flight features is a promising direction. Existing approaches build classifiers that consider their features to be correlated. This prevents a fine-grained detection of degradation for the different hardware components. This work presents an approach where the data is considered uncorrelated and, using machine learning techniques, allows the precise identification of UAV's damages.
In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous action state environment. We present a framework based on OpenAI GYM, Gazebo and RotorS MAV simulator, utilized for successfully training different agents to perform various tasks. The deep reinforcement learning method used for the training is model-free, on-policy, actor-critic based algorithm called Trust Region Policy Optimization (TRPO). Two neural networks have been used as a nonlinear function approximators. Our experiments showed that such learning approach achieves successful results, and facilitates the process of controller design.
Abstract-The usages and functionalities of Unmanned Aerial Vehicles (UAV) have grown rapidly during the last years. They are being engaged in many types of missions, ranging from military to agriculture passing by entertainment and rescue or even delivery. Nonetheless, for being able to perform such tasks, UAVs have to navigate safely in an often dynamic and partly unknown environment. This brings many challenges to overcome, some of which can lead to damages or degradations of different body parts. Thus, new tools and methods are required to allow the successful analysis and identification of the different threats that UAVs have to manage during their missions or flights. Various approaches, addressing this domain, have been proposed. However, most of them typically identify the changes in the UAVs behavior rather than the issue. This work presents an approach, which focuses not only on identifying degradations of UAVs during flights, but estimate the source of the failure as well.
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