Abstract
Unmanned Aerial Vehicles (UAVs) have many important applications in both civil and military areas. One of the most popular type of UAVs is quad–copter which uses four propellers to carry out its flight process. In this paper, a new control model which helps quad-copter to automatically find path and avoid obstacles indoor is introduced. The challenge of this model for quad-copter is the complex indoor environments with obstacles. Base on Deep Reinforcement Learning and Deep Learning platform, state of the art algorithms in Artificial Intelligence (AI), a new Ensemble model is proposed. The proposed model uses two algorithms to control quad-copter. One is quad-copter path finding algorithm (Deep Learning - ResNet8) and the other algorithm (Deep Reinforcement Learning - Res-DQN) dealing with obstacle avoidance. The output of both two algorithms are combined to change the direction of quad-copter adaptively with indoor environments. The simulation results have been assessed to verify the numerous performance of proposed control model.
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