An IoT-enabled reinforcement learning-driven ground robot for precision navigation and smart interaction in dynamic environments

Autonomous ground robots are increasingly relied upon in dynamic environments where reliable navigation and context-aware interaction are essential. However, existing robotic control systems often rely on cloud-based reinforcement learning (RL) frameworks or static algorithms that fail to adapt in real-time to noisy, unpredictable scenarios. These models typically overlook the constraints of edge deployment and lack robust integration with human interaction modalities such as voice and semantic object awareness. To address these limitations, this work proposes a fully embedded, IoT-enabled ground robot powered by a reinforcement learning-based adaptive control framework. The system leverages Raspberry Pi 4B+ as its core computational unit, integrating MQTT-driven communication, multimodal interaction through speech and vision, and lightweight policy convergence for obstacle-aware navigation. A novel RL-based state-action pipeline is trained and deployed entirely on-device, ensuring real-time responsiveness without external computation. Experimental evaluations show that the proposed framework reduces navigation errors by 22% and improves interaction latency by 37% over traditional PID and A*-based systems. The RL model converges in under 2200 episodes, with stable reward curves and high reliability across variable acoustic and physical terrains. This study showcases how low-cost, edge-based robots can achieve high autonomy and situational awareness contributing to future advancements in resilient, self-adaptive robotic systems within smart and resource-constrained environments.