The convergence of AI, machine learning, and sensor technologies has contributed to the advancement in robotics, enabling autonomous operation in complex and dynamic environments. Conventional control algorithms lack flexibility and adaptive decision-making, whereas deep reinforcement learning leverages multi-dimensional sensor data to learn complex behaviors. We developed and evaluated a DRL-based robot control system that operates under severe and uncertain conditions. The system integrated sensitive environmental sensor data with enhanced neural network architectures, including convolutional and recurrent layers, and employed a hybrid model-free/model-based agent design to balance learning efficiency with adaptability. Simulation and statistical analysis results demonstrated consistent learning patterns, with a running average reward stabilizing at −1177.28 (a standard deviation of 131.98), showing policy convergence. Evaluation across 50 episodes yielded a mean reward of −1333.46 (a standard deviation of 210.58), below the predefined success threshold of −200. These results show the developed system’s ability to learn navigation behaviors while underscoring the need for further optimization. The architecture contributes to sensor technology by enabling active sensing, multimodal integration, and compensation for material-level nonlinearities, supporting the development of cost-efficient, sustainable sensors and advancing reliable, adaptive autonomous robotic control systems.