Future Image Prediction for Mobile Robot Navigation: Front-Facing Camera Versus Omni-Directional Camera

Yu Ishihara, Masaki Takahashi

研究成果: Conference contribution

抄録

When we perform a task, we select the action by imagining its future consequences. Hence, the ability to predict future states would also be an essential feature for robotic agents because it would allow them to plan effective actions to accomplish given tasks. In this research, we explore an action-conditioned future image prediction model considering its application to navigation tasks for a mobile robot. We investigate the image prediction performance of deep neural network architectures and training strategies with two different camera systems. One camera system is a conventional front-facing camera that has a narrow field of view with high definition, and the other is an omni-directional camera that has a wide field of view with low definition. We compare the performances of prediction models for these two camera systems, and propose using an image prediction model with the omni-directional camera for the navigation tasks of a robot. We evaluate the prediction performance of each camera system through experiments conducted in a complex living room-like environment. We demonstrate that models with an omni-directional camera system outperform models with a conventional front-facing camera. In particular, the model comprising a combination of action-conditioned long short-term memory successfully predicts future images for states of more than 100 steps ahead in both simulation and real-world scenarios. Further, by integrating the proposed system into image-prediction-based navigation algorithm, we demonstrate that navigation based on a model with an omni-directional camera can successfully navigate the robot in cases where one with a conventional front-facing camera fails.

本文言語English
ホスト出版物のタイトルIntelligent Autonomous Systems 16 - Proceedings of the 16th International Conference IAS-16
編集者Marcelo H. Ang Jr, Hajime Asama, Wei Lin, Shaohui Foong
出版社Springer Science and Business Media Deutschland GmbH
ページ654-669
ページ数16
ISBN(印刷版)9783030958916
DOI
出版ステータスPublished - 2022
イベント16th International Conference on Intelligent Autonomous Systems, IAS-16 2020 - Virtual, Online
継続期間: 2021 6月 222021 6月 25

出版物シリーズ

名前Lecture Notes in Networks and Systems
412 LNNS
ISSN(印刷版)2367-3370
ISSN(電子版)2367-3389

Conference

Conference16th International Conference on Intelligent Autonomous Systems, IAS-16 2020
CityVirtual, Online
Period21/6/2221/6/25

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 信号処理
  • コンピュータ ネットワークおよび通信

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