Now showing items 1-4 of 4

    • A new deep reinforcement learning architecture for autonomous UAVs 

      Muñoz Ferran, Guillem (Universitat Politècnica de Catalunya, 2018-09-07)
      Bachelor thesis
      Open Access
      Recent improvements in computation and algorithmic research, together with the rising era of Big Data, have allowed Artificial Intelligence increase its popularity within masses. The recent publication of the Deep Q-Network ...
    • Deep reinforcement learning for drone delivery 

      Muñoz Ferran, Guillem; Barrado Muxí, Cristina; Cetin, Ender; Salamí San Juan, Esther (Multidisciplinary Digital Publishing Institute (MDPI), 2019-09-10)
      Open Access
      Drones are expected to be used extensively for delivery tasks in the future. In the absence of obstacles, satellite based navigation from departure to the geo-located destination is a simple task. When obstacles are known ...
    • Drone navigation and avoidance of obstacles through deep reinforcement learning 

      Cetin, Ender; Barrado Muxí, Cristina; Muñoz Ferran, Guillem; Macias López, Miquel; Pastor Llorens, Enric (Institute of Electrical and Electronics Engineers (IEEE), 2019)
      Conference report
      Open Access
      Unmanned aerial vehicles (UAV) specifically drones have been used for surveillance, shipping and delivery, wildlife monitoring, disaster management etc. The increase on the number of drones in the airspace worldwide will ...
    • Self-training by reinforcement learning for full-autonomous drones of the future 

      Kersandt, Kjell; Muñoz Ferran, Guillem; Barrado Muxí, Cristina (Institute of Electrical and Electronics Engineers (IEEE), 2018)
      Conference report
      Restricted access - publisher's policy
      Drones are rapidly increasing their activity in the airspace worldwide. This expected growth of the number of drones makes human-based traffic management prohibitive. Avionics systems able to sense-and-avoid obstacles ...