Reinforcement learning in autonomous navigation: Overcoming challenges in dynamic and unstructured environments

Autonomous navigation in dynamic and unstructured environments presents significant challenges due to the unpredictability of obstacles, varying terrains, and the need for real-time decision-making. Traditional navigation methods often struggle to address these complexities, highlighting the need for more adaptive and robust approaches. This paper critically reviews the application of reinforcement learning (RL) in autonomous navigation, emphasizing its strengths in learning from experience and adapting to new situations. However, RL alone may not be sufficient to meet the demands of real-time navigation in rapidly changing environments. The paper proposes a novel framework that combines reinforcement learning with adaptive planning algorithms to address this gap. This integration leverages the adaptability of RL and the structured decision-making of adaptive planning, resulting in enhanced navigation performance, particularly in challenging and unstructured environments. The proposed framework is expected to improve autonomous navigation systems' robustness, efficiency, and scalability. The paper concludes by discussing the potential applications of this framework across various domains, including robotics, autonomous vehicles, and exploration, and suggests directions for future research to refine further and validate the approach. Keywords: Autonomous Navigation, Reinforcement Learning, Adaptive Planning, Dynamic Obstacles, Unstructured Environments