Hybrid IoT-Cloud Control Framework for Human-Robot Collaboration in Cartesian Storage Robots: Design, Implementation, and Statistical Performance Improvement

Autonomous robots demonstrate high efficiency and precision in repetitive and structured tasks; however, their flexibility is often limited in dynamic or unstructured environments. Conversely, manually controlled systems allow human adaptability and situational awareness but tend to suffer from slower operation and inconsistent performance. To address this imbalance, this study evaluates the viability of a Firebase-based IoT–cloud architecture for real-time Cartesian robot control within a human–robot collaboration framework. The framework was implemented on a Cartesian robot prototype using an ESP32-based controller, with task commands synchronized through Firebase Realtime Database to support hybrid switching between autonomous execution and human teleoperation. Experimental evaluation involving 576 trials demonstrates that the hybrid framework achieves a perfect task success rate of 100%, outperforming manual operation (99.65%). It also reduces the average task completion time by 12.02%, with improvements ranging from 4.93% to 23.43% across individual tasks. Critically, the hybrid system significantly enhances operational consistency, reducing performance variability by 54% to 161% compared to manual control, as confirmed by statistical tests (Bonett and Levene's tests, p < 0.05). Cloud communication latency remains stable below 200 ms, ensuring reliable real-time operation. Despite the promising results, the experimental evaluation is limited to a single-robot setup under controlled network conditions, which may not fully represent large-scale or highly dynamic industrial environments. This work demonstrates that the proposed hybrid framework effectively combines human flexibility with robotic efficiency and consistency, offering a scalable and robust solution for adaptive warehouse automation that can be extended to broader warehouse and logistics applications involving remote supervisory control.