Target Search Based on Scene Priors

DOI
  • Lu Shengyang
    School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Liang Lanjun
    School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Zhao Huailin
    School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • Zhou Fangbo
    School of Electrical and Electronic Engineering, Shanghai Institute of Technology
  • yao Feng
    School of Electrical and Electronic Engineering, Shanghai Institute of Technology

Description

Aiming at the problems of reinforcement learning algorithm in target search tasks, such as low accuracy and low fault tolerance, this article mainly introduces a method of reinforcement learning target search based on scene prior in simulation environment. This method mainly uses graph convolutional neural network to extract the current object relationship as the input of prior knowledge. Secondly, it uses the actor-critic algorithm to take the agent's vision, position and prior knowledge as input to decide the agent's next navigation. Finally, use path planning to navigate to the target point to find the target. Through experiments conducted in Habitat and compared with the previous algorithm, the experiment shows that this method is better than the previous algorithm in target search accuracy and navigation efficiency.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390291767555852032
  • DOI
    10.5954/icarob.2022.os33-2
  • ISSN
    21887829
  • Text Lang
    en
  • Data Source
    • JaLC
    • Crossref
  • Abstract License Flag
    Disallowed

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