Enhancing Autonomous Robot Navigation Based on Deep Reinforcement Learning: Comparative Analysis of Reward Functions in Diverse Environments

Autonomous robot navigation in complex environments presents a significant challenge due to efficient decision-making for reaching goals and avoiding obstacles. This paper addresses this issue through the use of deep reinforcement learning techniques and a comprehensive analysis of reward functions and their impact on autonomous navigation. The study emphasizes the importance of selecting the most effective reward functions to achieve maximum robot performance in a variety of scenarios. Moreover, we propose a new reward mechanism that enables the robot to avoid collisions when objects move faster than the robot, resulting in the robot halting its motion to allow the object to pass before resuming its course. The effectiveness of these reward functions is validated through simulations, providing valuable insights into the robustness of robot navigation. Further details and simulations can be found in the following link: https://youtu.be/pPQDc25vj1U