A. Sivaranjani1,*, B. Vinod2
Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1135-1150, 2023, DOI:10.32604/iasc.2023.028126
Abstract Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a mobility robot’s best path prediction… More >
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