Indoor mobile robot navigation using deep convolutional neural network

Sleaman W. K., Yavuz S.

Journal of Intelligent and Fuzzy Systems, vol.39, pp.5475-5486, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39
  • Publication Date: 2020
  • Doi Number: 10.3233/jifs-189030
  • Journal Name: Journal of Intelligent and Fuzzy Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.5475-5486
  • Keywords: Deep reinforcement learning, autonomous agent, adaptive agent, autonomous exploration, control mobile robot, deep convolutional neural network, LOCALIZATION
  • Yıldız Technical University Affiliated: Yes


© 2020-IOS Press and the authors.Robot can help human in their everyday life and routine. These are not an indoor robot which was designed to perform desired task, but they can adapt to our environment by themselves and to learn from their own experiences. In this research we focus on high degree of autonomy, which is a must for social robots. For training purpose autonomous exploration and unknown environments is used along with proper algorithm so that robot can adapt to unknown environments. For testing purpose, simulation is carried with sensor fusion method, so that real world noise can be reduced and accuracy can be increased. This dissertation focuses on the intelligent robot control in autonomous navigation tasks and investigates the robot learning in following aspects. This method is based on human instinct of imitation. In this standard real time data set is provided to the robot for training purpose, it gets train from these data and generalize over all unseen potential situations and environments. Convolutional Neural Network is used to determine the probability and based on that robot can act. After acceptable number of demonstrations, robot can predict output with high accuracy and hence can acquire the independent navigation skills. State-of-the-art reinforcement learning techniques is used to train the robot via interaction with the robots. Convolutional Neural Network is also incorporated for fast generalization. Robot is train based on all past state-action pairs collected during interaction. This training model can predict output which helps robot for autonomous navigation.