Simulation and Evaluation of Deep Learning Autoencoders for Image Compression in Multi-UAV Network Systems

  • Gabryel Silva Ramos CEFET-RJ
  • Amaro Azevedo De Lima CEFET-RJ
  • Luciana F. Almeida CEFET-RJ
  • Jose Lima Instituto Politécnico de Bragança
  • Milena Faria Pinto CEFET-RJ

Resumo


Mobile multi-robot systems are versatile alternatives for improving single-robot capacities in many applications, such as logistics, environmental monitoring, search and rescue, photogrammetry, etc. In this sense, this kind of system must have a reliable communication network between the vehicles, ensuring that information exchanged within the nodes has little losses. This work simulates and evaluates the use of autoencoders for image compression in a multi-UAV simulation with ROS and Gazebo for a generic surveillance application. The autoencoder model was developed with the Keras library, presenting good training and validation results, with training and validation accuracy of 70%, and a Peak Signal Noise Ratio (PSNR) of 40dB. The use of the CPU for the simulated UAVs for processing and sending compressed images through the network is 25% faster. The results showed that this compression methodology is a good choice for improving the system’s performance without losing too much information.
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Publicado
09/10/2023
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RAMOS, Gabryel Silva; LIMA, Amaro Azevedo De; ALMEIDA, Luciana F.; LIMA, Jose; PINTO, Milena Faria. Simulation and Evaluation of Deep Learning Autoencoders for Image Compression in Multi-UAV Network Systems. In: SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO AMERICANO DE ROBÓTICA (SBR/LARS), 15. , 2023, Salvador/BA. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 41-46.