Predictive Analytics for Energy Consumption of Autonomous Mobile Robot Using Hybrid ARIMA-XGBoost

Authors

  • Pipit Anggraeni Politeknik Manufaktur Bandung
  • Wahyu Adhie Candra Politeknik Manufaktur Bandung
  • Surya Dharma Jatnika Politeknik Manufaktur Bandung
  • Noval Lilansa Politeknik Manufaktur Bandung
  • Adhitya Sumardi Sunarya Politeknik Manufaktur Bandung
  • Nur Jamiludin Ramadhan Politeknik Manufaktur Bandung
  • Andri Wiyono Politeknik Manufaktur Bandung

DOI:

10.33395/sinkron.v10i3.16484

Keywords:

ARIMA, autonomous mobile robot, energy prediction, hybrid model, XGBoost

Abstract

The deployment of autonomous mobile robots in smart manufacturing and intralogistics has grown rapidly, yet current battery management systems can only monitor real-time charge levels without predicting future energy consumption. This reactive limitation risks mid-mission battery depletion and production disruption. The study presents a predictive analytics system for the Polebot autonomous mobile robot integrated with a Robot Operating System 2 data historian pipeline and an InfluxDB time-series database. The objective is to evaluate whether an autoregressive time-series model or a gradient-boosted machine learning model better suits different operational conditions, specifically constant-velocity static operation versus acceleration-heavy dynamic operation. Data were collected from three sensor sources across six operational protocols covering baseline, high-load, stop-and-go, creep, burst acceleration, and mixed conditions, yielding 10,800 synchronized data points at 1 Hz after resampling. Results show that the Autoregressive Integrated Moving Average model with parameters (2,1,3) achieves a Mean Absolute Error of 1.047% and a symmetric Mean Absolute Percentage Error of 1.74% for battery State of Charge prediction under static conditions. Extreme Gradient Boosting achieves a Mean Absolute Error of 0.022 watts for motor power prediction, 136 times more accurate than the time-series model for the same variable. The proposed Condition-Based Temporal Switching framework was validated on 1,803 data points and autonomously produced 265 model transitions during a 30-minute mixed operational test, with static conditions comprising 83.9% of the validation window. Adaptive model selection outperforms single-model strategies for energy prediction in autonomous mobile robot platforms.

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References

Abdelmeguid, Y., & Hasan, A. (2026). Data-Driven Autoregressive Power Prediction for GTernal Robots in the Robotarium. Retrieved from http://arxiv.org/abs/2603.13908

Caballero, L., Perafan, Á., Rinaldy, M., & Percybrooks, W. (2021). Predicting the energy consumption of a robot in an exploration task using optimized neural networks. Electronics (Switzerland), 10(8). https://doi.org/10.3390/electronics10080920

Chang, Q., Yuan, T., Li, H., Chen, Y., Wang, X., Gao, S., … Wang, L. (2024). A Data-Driven Method for Predicting and Optimizing Industrial Robot Energy Consumption Under Unknown Load Conditions. Actuators, 13(12). https://doi.org/10.3390/act13120516

Fei, C., Lu, Z., Jiang, W., Zhao, L., & Zhang, F. (2025). Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization. Batteries, 11(6). https://doi.org/10.3390/batteries11060207

Fragapane, G., de Koster, R., Sgarbossa, F., & Strandhagen, J. O. (2021, October 16). Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda. European Journal of Operational Research, Vol. 294, pp. 405–426. Elsevier B.V. https://doi.org/10.1016/j.ejor.2021.01.019

Góra, K., Granosik, G., & Cybulski, B. (2024, July 1). Energy Utilization Prediction Techniques for Heterogeneous Mobile Robots: A Review. Energies , Vol. 17. Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/en17133256

Góra, K., Kujawinski, M., Wroński, D., & Granosik, G. (2021). Comparison of energy prediction algorithms for differential and skid‐steer drive mobile robots on different ground surfaces. Energies, 14(20). https://doi.org/10.3390/en14206722

Hou, L., Zhou, F., Kim, K., & Zhang, L. (2021). Practical model for energy consumption analysis of omnidirectional mobile robot. Sensors, 21(5), 1–18. https://doi.org/10.3390/s21051800

Jafari, S., Shahbazi, Z., Byun, Y. C., & Lee, S. J. (2022). Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach. Mathematics, 10(6). https://doi.org/10.3390/math10060888

Krot, K., Iskierka, G., Poskart, B., & Gola, A. (2022). Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology. Materials, 15(19). https://doi.org/10.3390/ma15196561

Lei, C. (2024). New energy vehicle battery state of charge prediction based on XGBoost algorithm and RF fusion. Energy Informatics, 7(1). https://doi.org/10.1186/s42162-024-00424-1

Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022, May 1). Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, Vol. 7. American Association for the Advancement of Science. https://doi.org/10.1126/scirobotics.abm6074

Nonoyama, K., Liu, Z., Fujiwara, T., Alam, M. M., & Nishi, T. (2022). Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization. Energies, 15(6). https://doi.org/10.3390/en15062074

Pierre, A. A., Akim, S. A., Semenyo, A. K., & Babiga, B. (2023). Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches. Energies, 16(12). https://doi.org/10.3390/en16124739

Poskart, B., Iskierka, G., Krot, K., Burduk, R., Gwizdal, P., & Gola, A. (2022). Multi-Parameter Predictive Model of Mobile Robot’s Battery Discharge for Intelligent Mission Planning in Multi-Robot Systems. Sensors, 22(24). https://doi.org/10.3390/s22249861

Visca, M., Powell, R., Gao, Y., & Fallah, S. (2022). Probabilistic Meta-Conv1D Driving Energy Prediction for Mobile Robots in Unstructured Terrains. IEEE Access, 10, 107913–107928. https://doi.org/10.1109/ACCESS.2022.3209259

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How to Cite

Anggraeni, P., Adhie Candra, W., Jatnika, S. D. ., Lilansa, N., Sumardi Sunarya, A., Jamiludin Ramadhan, N., & Wiyono, A. (2026). Predictive Analytics for Energy Consumption of Autonomous Mobile Robot Using Hybrid ARIMA-XGBoost. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(3), 1973-1983. https://doi.org/10.33395/sinkron.v10i3.16484

Issue

Vol. 10 No. 3 (2026): Article Research July 2026

Section

Articles

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Copyright (c) 2026 Pipit Anggraeni, Wahyu Adhie Candra, Surya Dharma Jatnika, Noval Lilansa, Adhitya Sumardi Sunarya, Nur Jamiludin Ramadhan, Andri Wiyono

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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