Affiliation:
1. Faculty of Electrical and Electronic Engineering, University of Transport and Communications, Hanoi 100000, Vietnam
2. Faculty of Control and Automation, Electric Power University, Hanoi 100000, Vietnam
Abstract
This study provides simulation and experimental results on techniques for avoiding static and dynamic obstacles using a deep Q-learning (DQL) reinforcement learning algorithm for a two-wheel mobile robot with independent control. This method integrates the Q-learning (QL) algorithm with a neural network, where the neural networks in the DQL algorithm act as approximators for the Q matrix table for each pair (state–action). The effectiveness of the proposed solution was confirmed through simulations, programming, and practical experimentation. A comparison was drawn between the DQL algorithm and the QL algorithm. Initially, the mobile robot was connected to the control script using the Robot Operating System (ROS). The mobile robot was programmed in Python within the ROS operating system, and the DQL controller was programmed in Gazebo software. The mobile robot underwent testing in a workshop with various experimental scenarios considered. The DQL controller displayed improvements in computation time, convergence time, trajectory planning accuracy, and obstacle avoidance. As a result, the DQL controller surpassed the QL algorithm in terms of performance.
Subject
Control and Optimization,Control and Systems Engineering
Reference33 articles.
1. A chaotic path planning generator for autonomous mobile robots;Volos;Robots Auton. Syst.,2012
2. SmartPATH: An efficient hybrid ACO-GA algorithm for solving the global path planning problem of mobile robots;Trigui;Int. J. Adv. Robot. Syst.,2014
3. An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system;Gharajeh;Ain Shams Eng. J.,2021
4. Path planning and collision avoidance for autonomous surface vehicles I: A review;Vagale;J. Mar. Sci. Technol.,2021
5. Zhang, C., Zhou, L., Li, Y., and Fan, Y. (2020). A dynamic path planning method for social robots in the home environment. Electronics, 9.