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Abstract: Autonomous navigation in unstructured environments poses a significant challenge in robotics and artificial intelligence. The capability to navigate through dynamic and unpredictable terrains such as disaster zones, outdoor landscapes, or congested urban settings demands sophisticated solutions. This research paper delves into the imperative role of Deep Reinforcement Learning (DRL) in addressing these challenges and advancing the field of autonomous navigation. The core necessity of this research paper lies in the application and exploration of DRL within the realm of autonomous navigation. By leveraging neural networks and reinforcement learning algorithms, autonomous agents can dynamically navigate through unstructured environments without explicit programming or human intervention. Instead, they learn to navigate by receiving feedback through rewards or penalties, thereby continuously improving their decision-making processes. Through a comprehensive review of existing literature and experiments this paper aims to elucidate the pivotal role of DRL in shaping the future of autonomous navigation. It highlights the necessity of robust and adaptive systems capable of navigating unstructured environments, emphasizing the transformative potential of DRL in revolutionizing autonomous systems' capabilities.
Khan et al. (Sun,) studied this question.