Research on Autonomous Navigation and Control Algorithm of Intelligent Robot based on Reinforcement Learning

Abstract

The last few decades have seen impressive developments in the field of robotics, especially in the areas of autonomous navigation and control. Robust algorithms that can facilitate effective decision-making in real-time settings are needed as the need for intelligent robots that can function in complex and dynamic contexts grows. Through trial-and-error interactions with their surroundings, reinforcement learning (RL) has become a promising method for teaching intelligent agents to navigate and control robots independently. The purpose of this study is to look at the creation and use of reinforcement learning algorithms for intelligent robot control and autonomous navigation. With an emphasis on methods like deep Q-learning, policy gradients, and actor-critic approaches, the research delves into the theoretical underpinnings of reinforcement learning and how it has been applied to the field of robotics. This study assesses how well RL algorithms work to help robots acquire the best navigational strategies in challenging surroundings through an extensive literature review and empirical investigation. In addition, the study suggests new improvements and optimizations for current reinforcement learning algorithms to tackle problems unique to robot navigation, such as avoiding obstacles, routing, and interactions with dynamic environments. These improvements increase the effectiveness, flexibility, and security of independent robot navigation systems by utilizing knowledge from cognitive science and neuroscience. The suggested methods are experimentally evaluated through both real-world applications on physical robotic platforms and simulation-based research. Performance measures including navigation speed, success rate, and collision avoidance ability are used to evaluate how well the suggested algorithms operate in different scenarios and circumstances.