Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—This paper presents navigation for four-wheel Omni Robot using architecture deep reinforcement learning in an unknown environment. A deep reinforcement learning algorithm combines with effectively training data using stochastic gradient updates in order to connect a goal. The approach is simulated and visualized using Gazebo and is updated via policies trained by deep Q learning network. Using recent deep-learning techniques as the basis of the framework, our results indicate that it is capable of providing smooth navigation for the Omni robot in exploring unpredicted surroundings. Once extended to real-world operation, this framework could enable the Omni Robot to gain achievement for self-driving tasks.
Copyright © 2015-2023 International Journal of Mechanical Engineering and Robotics Research, All Rights Reserved