In this paper, we propose an efficient navigation framework for autonomous mobile robots in dynamic environments using a combination of a reinforcement learning algorithm and a neural network model. The main idea of the proposed algorithm is to provide the mobile robots the relative position and motion of the surrounding objects to the robots, and the safety constraints such as minimum distance from the robots to the obstacles, and a learning model. We then distribute the mobile robots into a dynamic environment. | Journal of Computer Science and Cybernetics, , (2017), 107–118 DOI AN EFFICIENT NAVIGATION FRAMEWORK FOR AUTONOMOUS MOBILE ROBOTS IN DYNAMIC ENVIRONMENTS USING LEARNING ALGORITHMS XUAN-TUNG TRUONG, HONG TOAN DINH, CONG DINH NGUYEN Department of Automation and Computer Engineering, Faculty of Control Engineering, Le Quy Don Technical University, Vietnam , , dinhnc@ Abstract. In this paper, we propose an efficient navigation framework for autonomous mobile robots in dynamic environments using a combination of a reinforcement learning algorithm and a neural network model. The main idea of the proposed algorithm is to provide the mobile robots the relative position and motion of the surrounding objects to the robots, and the safety constraints such as minimum distance from the robots to the obstacles, and a learning model. We then distribute the mobile robots into a dynamic environment. The robots will automatically learn to adapt to the environment by their own experience through the trial-and-error interaction with the surrounding environment. When the learning phase is completed, the mobile robots equipped with our proposed framework are able to navigate autonomously and safely in the dynamic environment. The simulation results in a simulated environment show that, our proposed navigation framework is capable of driving the mobile robots to avoid dynamic obstacles and catch up dynamic targets, providing the safety for the surrounding objects and the mobile robots. Keywords. Autonomous mobile robot, mobile robot navigation, reinforcement learning, q-learning. 1. INTRODUCTION The ability to autonomously navigate in dynamic environments, such as urban and terrain environments, museums, airports, offices and homes, and shopping malls, is crucial for mobile robots. If we wish to deploy the autonomous mobile robots in such environments, the first and most important issue is
