KEYWORDS: Data modeling, Visualization, RGB color model, Network architectures, 3D modeling, Detection and tracking algorithms, Monte Carlo methods, Machine learning, Visual process modeling
Deep reinforcement learning has greatly simplified visual navigation by utilizing the end-to-end network training strategy. Unlike previous navigation methods which build upon high-precision maps, deep reinforcement learning-based method enables real-time navigation by only taking one image as input at a time. As such, deep reinforcement learning based navigation methods are applicable to a variety number of applications in robotics/vision communities, thanks to its light-weight computational cost. Despite the advantages, however, these methods still suffer from inefficient data exploration and poor convergence on network training. In this paper, we propose to use inverse reinforcement learning to solve the problem,which can provide more accurate and efficient guidance for decision-making. The proposed method is able to learn a more effective reward function from less training data. Experiments demonstrated that the proposed method achieves a higher success rate of navigation and produces paths that are more similar to the optimal ones compared to the reinforcement learning baselines.
The quadcopter has been widely used in the field of aerial photography and environmental detection, because of its advantages of VTOL, simple structure, and easy-control. In the field of urban anti-terrorism or special operations, micro reconnaissance quadcpter has its unique advantages such as all-weather taking off and landing, small noise and so on, and it is very popular with special forces and riot police. This paper aims at the flight control problem of the micro quadcopter, for the purposes of attitude stabilization control and trajectory tracking control of the micro quadcopter, first, the modeling of the micro quadcopter is presented. And using the MATLAB/SIMULINK toolbox to build the flight controller of the micro quadcopter, and then simulation analysis and real flight test are given. The results of the experiment show that the designed PID controller can correct the flight attitude shift effectively and track the planned tracks well, and can achieve the goal of stable and reliable flight of the quadcopter. It can be a useful reference for the flight control system design of future special operations micro UAV.
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