Ld
L.S. den Ridder
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Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate the effects of learning how to synthesize one view from the other in a stereo-vision setup without relying on any preliminary knowledge of the camera extrinisics and how it can be integrated for its downstream use for an obstacle avoidance task. As such, the literature study concludes that competitive geometry-free monocular-to-stereo image view synthesis is feasible due to recent developments in computer vision. The scientific paper further develops concepts proposed in the literature study and benchmarks the proposed architectures on depth estimation benchmarks for KITTI. Competitive results are achieved for view synthesis and despite sub-optimal performance compared to state-of-the-art monocular depth estimation, an ability to encode depth and detect shapes is present and, therefore, satisfactory for the application to DRL. Additionally, the research examines the benefits of using the latent space of a view synthesis architecture compared to other feature extractor methods as an input to the PPO agent implemented as auxiliary tasks. This method achieves quicker convergence and better performance for an obstacle avoidance task in a simulated indoor environment than the autoencoding feature extractor and end-to-end DRL methods. It is only outperformed by the monocular depth estimation feature extractor method. Overall, this research provides valuable insights for developing more efficient and effective DRL methods for monocular camera-based drones. Finally, the complementary code for this research can be found: \url{https://github.com/ldenridder/drl-obstacle-avoidance-view-synthesis}.
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Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate the effects of learning how to synthesize one view from the other in a stereo-vision setup without relying on any preliminary knowledge of the camera extrinisics and how it can be integrated for its downstream use for an obstacle avoidance task. As such, the literature study concludes that competitive geometry-free monocular-to-stereo image view synthesis is feasible due to recent developments in computer vision. The scientific paper further develops concepts proposed in the literature study and benchmarks the proposed architectures on depth estimation benchmarks for KITTI. Competitive results are achieved for view synthesis and despite sub-optimal performance compared to state-of-the-art monocular depth estimation, an ability to encode depth and detect shapes is present and, therefore, satisfactory for the application to DRL. Additionally, the research examines the benefits of using the latent space of a view synthesis architecture compared to other feature extractor methods as an input to the PPO agent implemented as auxiliary tasks. This method achieves quicker convergence and better performance for an obstacle avoidance task in a simulated indoor environment than the autoencoding feature extractor and end-to-end DRL methods. It is only outperformed by the monocular depth estimation feature extractor method. Overall, this research provides valuable insights for developing more efficient and effective DRL methods for monocular camera-based drones. Finally, the complementary code for this research can be found: \url{https://github.com/ldenridder/drl-obstacle-avoidance-view-synthesis}.
Urbanergy WindVine
Wind energy system integrated between high-rise buildings
Bachelor thesis
(2020)
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Vladimir Fayt, A.K.I. Follet, M.J. Loonen, R.W. Norbruis, A.T.F. Postma, L.S. den Ridder, L.W. Schwarzpaul, J.M. Stensen, K.V.A. Vleeschouwer, N. Eleftheroglou, L.J. Kootte, F. Avallone
The demand for renewable energy has increased significantly over the past years. During the last decades it became clear that the main source of renewable energy will be wind energy. The wind turbines, still growing in size every year, are almost all located in either remote rural or offshore areas. However, there lies a huge opportunity in imple-menting wind harvest systems in urban areas.
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The demand for renewable energy has increased significantly over the past years. During the last decades it became clear that the main source of renewable energy will be wind energy. The wind turbines, still growing in size every year, are almost all located in either remote rural or offshore areas. However, there lies a huge opportunity in imple-menting wind harvest systems in urban areas.