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Deep cross altitude visual interpretation for service robotic agents in smart city

Haji Abbasi, M ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1109/CFIS.2018.8336636
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2018
  4. Abstract:
  5. Multi-agent robotic platforms are increasingly used for various commercial applications. In this paper, a cross altitude visual analytic framework for a group of robots, singularly referred to as MOdular RApidly Deployable Decision Support Agent (MORAD DSA), used for decision support and various services in the smart city environment is presented. The robotic subsystem consists of two agents operating in different altitudes. These agents give the decision support system the ability to have encompassing view of the operating environment. The visual analytic system which is the focus of this paper uses a deep convolutional neural network to learn the complex patterns required by the urban management responsibilities. Several smart city applications such as sidewalk pavement inspection, sidewalk sweeping, rubbish detection and parks management scenarios are used for real world simulation of the proposed framework. The experimental results show that the proposed algorithm is capable of performing complex inspection and decision-making tasks required for smart city management. © 2018 IEEE
  6. Keywords:
  7. Machine learning ; Complex networks ; Computer vision ; Decision support systems ; Deep learning ; Deep neural networks ; Intelligent systems ; Learning systems ; Multi agent systems ; Neural networks ; Pavements ; Robotics ; Smart city ; Visualization ; Commercial applications ; Deep convolutional neural networks ; Management scenarios ; Operating environment ; Pavement inspections ; Real-world simulation ; Smart city applications ; Visual interpretation ; Decision making
  8. Source: 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems ; Volume 2018-January , 2018 , Pages 79-82 ; 9781538628362 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/8336636