Reinforcement Learning-Based Autonomous Navigation Framework for Mobile Robots in Unstructured Environments

Abstract

Autonomous navigation is a fundamental capability for intelligent mobile robots operating in dynamic and unstructured environments such as disaster zones, industrial facilities, urban terrains, agricultural fields, and indoor service environments. Traditional navigation approaches based on rule-based systems, classical path planning algorithms, and handcrafted control strategies often struggle to adapt to uncertain environments, dynamic obstacles, and incomplete sensory information. These limitations reduce navigation robustness and flexibility in real-world robotic applications. Recent advancements in artificial intelligence and deep reinforcement learning (DRL) have provided promising solutions for autonomous decision-making and adaptive robotic navigation. This research proposes a Reinforcement Learning-Based Autonomous Navigation Framework for Mobile Robots in Unstructured Environments. The proposed framework integrates deep reinforcement learning, sensor fusion, environmental perception, and reward-driven policy optimization to enable intelligent robot navigation without explicit environmental modeling. The architecture combines convolutional neural networks for spatial feature extraction, reinforcement learning agents for decision-making, and dynamic obstacle avoidance strategies for adaptive path planning. The proposed framework enables mobile robots to learn optimal navigation policies through continuous interaction with complex environments. Reinforcement learning algorithms such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor–Critic (SAC) are employed to optimize navigation actions based on cumulative rewards. Experimental evaluation demonstrates that the proposed framework significantly improves navigation accuracy, obstacle avoidance efficiency, path optimality, and adaptability compared to traditional navigation systems. The framework also shows strong robustness in partially observable and dynamically changing environments.