Enhancing autonomous navigation systems through reinforcement learning

Conventional cleaning and waste-collecting robots face significant challenges when operating in dynamic and unpredictable environments. Traditional obstacle-avoidance algorithms often fail to account for moving objects, while existing learning-based navigation techniques demand extensive datasets and high computational resources, limiting their applicability in low-cost service robots. To address these limitations, this study proposes an adaptive autonomous navigation framework that integrates Imitation Learning (IL) with the Deep Deterministic Policy Gradient (DDPG) algorithm. The hybrid learning model accelerates policy acquisition through expert demonstrations and continuously refines navigation behavior via reinforcement feedback, ensuring real-time adaptability and robust obstacle avoidance in dynamic environments. The system was developed using ROS2 and evaluated within a Gazebo simulation environment. Experimental results across multiple validation trials demonstrate a consistent 23.7% improvement in path efficiency, an 18.4% reduction in collision rate, and 27% faster convergence compared to baseline reinforcement learning approaches, including standard DDPG and its variants. The proposed neural control architecture successfully generalizes across both indoor and outdoor navigation tasks, maintaining stability under sensor noise and environmental variations. These outcomes validate the feasibility of deploying the proposed framework in practical cleaning and waste management applications, particularly for resource-constrained robotic systems, offering enhanced autonomy, responsiveness, and energy efficiency.