Unmanned Aerial Vehicles (UAVs) are becoming more prevalent, more capable, and less expensive every day. Advances in battery life and electronic sensors have spurred the development of diverse UAV applications outside their original military domain. For example, Search and Rescue (SAR) operations stand to benefit greatly from modern UAVs since even the simplest commercial models are equipped with high-resolution cameras and the ability to stream video to a computer or portable device. As a result, autonomous unmanned systems (terrestrial, marine, and aerial) have begun to be employed for such typical SAR tasks as terrain mapping, task observation, and early supply delivery. However, these systems were developed before recent advances in artificial intelligence such as Google Deepmind’s breakthrough with the Deep Q-Network (DQN) technology. Therefore, most of them rely heavily on Greedy or Potential-based heuristics, without the ability to learn. In this research, we investigate a possible approximation (called Partially Observable Markov Decision Processes) for enhancing the performance of autonomous UAVs in SAR by incorporating newly-developed Reinforcement Learning methods. The project utilizes open-source tools such as Microsoft’s state-of-the-art UAV simulator AirSim, and Keras, a machine learning framework that can make use of Google’s popular tensor library called TensorFlow. The main approach investigated in this research is the Deep Q-Network.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning for Autonomous Search and Rescue




    Publication date :

    2018-07-01


    Size :

    1116595 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous search and rescue unmanned ship and autonomous search and rescue method

    DOU PEILIN / ZHAO JIAYI / LIU MENGGE et al. | European Patent Office | 2023

    Free access

    Autonomous Driving with Deep Reinforcement Learning

    Zhu, Yuhua / Technische Universität Dresden | SLUB | 2023


    Semi-Autonomous Search and Rescue System

    Walz, Eli / Hammonds, Katherine R. / Rumbaugh, Megan et al. | IEEE | 2023


    Autonomous vehicle navigation with deep reinforcement learning

    Cabañeros López, Àlex | BASE | 2019

    Free access

    Autonomous braking system via deep reinforcement learning

    Chae, Hyunmin / Kang, Chang Mook / Kim, ByeoungDo et al. | IEEE | 2017