Affiliations: [a] Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be University), Hyderabad, Telangana, India
| [b] Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana, India
| [c] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Correspondence:
[*]
Corresponding author. V. Sitharamulu, Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed to be University), Hyderabad, Telangana, India. Email: vsitaramu.1234@gmail.com.
Abstract: In this study, we investigate the viability of applying fuzzy reinforcement learning (FRL) to Internet of Things-based robots for purposes of autonomous navigation and collision avoidance. The proposed approach utilises FRL, IoT, and a sensor network to give the robot the ability to learn from its environment and act in accordance with those policies. The authors used FRL to train a mobile robot with wheels to move around and avoid obstacles, and then they put the robot through its paces in a virtual world. Results showed that the FRL-based technique improved the robot’s navigation and collision avoidance performance compared to traditional rule-based approaches. The results of this study indicate that FRL may be a viable technique for enabling autonomous navigation and obstacle avoidance in IoT-based robotics.
