Deep Reinforcement Learning for Adaptive Navigation in Unknown Terrains

Abstract

Autonomous navigation in unknown and unstructured terrains remains a critical challenge in robotics. Traditional mapping and planning approaches often require prior environmental knowledge, limiting real-time adaptability. Deep Reinforcement Learning (DRL), when combined with Simultaneous Localization and Mapping (SLAM), offers a transformative solution. This paper explores the implementation of DRL for adaptive path planning and obstacle avoidance in terrains where no prior map is available. The proposed approach empowers robotic agents to explore, learn, and navigate autonomously by interacting with the environment. We present a comprehensive framework integrating DRL algorithms, SLAM, and real-time sensor feedback to optimize navigation policies. The results demonstrate the agent’s ability to adapt dynamically, avoid obstacles, and achieve efficient path planning in previously unseen environments. The paper also includes original simulation data, 2D navigation models, and performance comparisons with conventional algorithms.

Keywords: Deep Reinforcement Learning, SLAM, Path Planning, Obstacle Avoidance, Autonomous Exploration, Unknown Terrain