A Safe Navigation Algorithm for Differential-Drive Mobile Robots by Using Fuzzy Logic Reward Function-Based Deep Reinforcement Learning

Abstract

Researchers are actively exploring advanced algorithms to enhance robots’ ability to navigate complex environments while avoiding obstacles. Four different environments were designed in the Webots simulator, including a mobile robot, a goal, a static obstacle, and one or two dynamic obstacles. The robot’s state vector was determined based on its position, the goal, and sensor variables, with all elements randomly placed in each learning and test step. A multi-layer perceptron (MLP) agent was trained for 1000 episodes in these environments using classical and fuzzy logic-based reward functions. After the training process was completed, the agents trained with the fuzzy logic-based reward function were tested for each environment. As a result of the test, while the robot’s arrival rate was 100% in the first three environments, it was measured as 91% in the fourth environment. In the last environment, the rate of crashing into a wall or dynamic obstacle was observed to be 7%. In addition, the agent trained in the fourth environment was found to successfully reach the target in multi-robot environments. The agent trained fuzzy logic-based reward function obtained the best result for four different environments. Based on these results, a fuzzy logic-based reward function was proposed to address the tuning problem of the classical reward function. It was demonstrated that a robust fuzzy logic-based reward function was successfully designed. This study contributed to the literature by presenting a reinforcement learning-based safe navigation algorithm incorporating a fuzzy logic-based reward function.