Department of System Design and Control EngineeringAutonomous navigation of unmanned aerial vehicles (UAVs) has posed several challenges due to the limitations regarding the number and size of sensors that can be attached to the mobile robots. Although sensors such as LIDARs that directly obtain distance information of the surrounding environment have proven to be effective for obstacle avoidance, the weight and cost of the sensor contribute to the restrictions on usage for UAVs as recent trends require smaller sizes of UAVs. One practical option is the utilization of monocular vision sensors which tend to be lightweight and have a relatively low cost, yet still the main drawback is that it is difficult to draw a certain rule from the sensor data. Conventional methods regarding visual navigation makes use of features within the image data or estimate the depth of the image using various techniques such as optical flow. These features and methodologies however still rely on human-based rules and features, meaning that robustness can become an issue.
A more recent approach to vision-based obstacle avoidance exploits heuristic methods based on artificial intelligence such as deep learning technologies, which have shown state-of-the-art performance in fields such as image processing or voice recognition. These technologies are capable of automatically selecting important features for classification or prediction tasks, hence allowing superior performance. Such heuristic methods have proven to be more efficient as the rules and features that are drawn from the image are automatically determined, unlike conventional methods where the rules and features are explicitly determined by humans.
In this thesis, we propose an imitation learning framework based on deep learning technologies that can be applied to the obstacle avoidance of UAVs, where the neural networks in this framework are trained upon the flight data obtained from human experts, extracting the necessary features and rules to carry out designated tasks. The system introduced in this thesis mainly consists of three parts: the data acquisition and preprocessing phase, the model training phase, and the model application phase. A CNN (Convolutional Neural Network), 3D-CNN, and a DNN (Deep Neural Network) will each be applied to the framework and tested with respect to the collision ratios to validate the obstacle avoidance performance.ope
