Autonomous and Collaborative Robots for Energy Systems: State-of-the-art Review
PDF
PDF

How to Cite

A., Pasumpon Pandian. 2026. “Autonomous and Collaborative Robots for Energy Systems: State-of-the-Art Review”. Journal of Electrical Engineering and Automation 7 (4): 286-303. https://doi.org/10.36548/jeea.2025.4.002.

Keywords

— Autonomous robots
— Collaborative robots
— Energy systems
— Energy efficiency
— Robotic inspection
— System integration
Published: 29-01-2026

Abstract

Energy systems are becoming larger, more complicated and distributed system. Such environments make manual operation and investigation based on unsafe, slow and inefficient operations. The independent and cooperative robots have been realized as viable systems in energy infrastructures to monitor, maintain and assist in operating the facilities. Recent research indicates a gradual increase in automated, energy-sensitive work and combined task performance of robots. Simultaneously, energy systems have severe limitations in safety, reliability and power efficiency. According to research explained in recent surveys, energy-efficient movement planning, smart power controlling and collaborative working among robots in energy-based tasks are most important. This paper reviews autonomous and collaborative robots in energy systems. It considers energy infrastructures as robotic workplaces, discusses autonomous and collaborative robots and reviews energy consumption modelling and optimization plans. The system integration and real-life implementations are also discussed. Finally, the main problems and future study directions are described. This survey aims to provide a brief and methodical source of data for researchers and engineers working on robotics and energy systems.

References

  1. Misaros, Marius, Ovidiu-Petru Stan, Ionut-Catalin Donca, and Liviu-Cristian Miclea. "Autonomous robots for services—state of the art, challenges, and research areas." Sensors 23, no. 10 (2023): 4962.
  2. Gils, Hans Christian, Hedda Gardian, Martin Kittel, Wolf-Peter Schill, Alexander Zerrahn, Alexander Murmann, Jann Launer et al. "Modeling flexibility in energy systems—comparison of power sector models based on simplified test cases." Renewable and Sustainable Energy Reviews 158 (2022): 111995.
  3. Wu, Mingyu, Che Fai Yeong, Eileen Lee Ming Su, William Holderbaum, and Chenguang Yang. "A review on energy efficiency in autonomous mobile robots." Robotic Intelligence and Automation 43, no. 6 (2023): 648-668.
  4. Patil, Swapnil, Vishwa Vasu, and K. V. S. Srinadh. "Advances and perspectives in collaborative robotics: a review of key technologies and emerging trends." Discover Mechanical Engineering 2, no. 1 (2023): 13.
  5. Sperling, Marvin, and Tommi Kivelä. "Concept of a dual energy storage system for sustainable energy supply of automated guided vehicles." Energies 15, no. 2 (2022): 479.
  6. Kim, Dongjun, Minho Choi, and Jumyung Um. "Digital twin for autonomous collaborative robot by using synthetic data and reinforcement learning." Robotics and Computer-Integrated Manufacturing 85 (2024): 102632.
  7. Miranda, Sofia, Carlos Renato Vázquez, and Manuel Navarro-Gutiérrez. "Energy consumption analysis and optimization in collaborative robots." Frontiers in Robotics and AI 12 (2025): 1671336.
  8. De Laet, Robbe, Nick Van Oosterwyck, Lorenzo Scalera, Annie Cuyt, Alessandro Gasparetto, and Stijn Derammelaere. "Energy-efficient motion planning for robotic systems using polynomials in the Chebyshev basis." Robotics and Autonomous Systems (2025): 105051.
  9. Miranda, Sofia, and Carlos Renato Vázquez. "Analysis and Prediction of Energy Consumption in a Collaborative Robot." IFAC-PapersOnLine 56, no. 2 (2023): 3710-3715.
  10. Nonoyama, Kazuki, Ziang Liu, Tomofumi Fujiwara, Md Moktadir Alam, and Tatsushi Nishi. "Energy-efficient robot configuration and motion planning using genetic algorithm and particle swarm optimization." Energies 15, no. 6 (2022): 2074.
  11. Jiang, Pei, Zuoxue Wang, Xiaobin Li, Xi Vincent Wang, Bodong Yang, and Jiajun Zheng. "Energy consumption prediction and optimization of industrial robots based on LSTM." Journal of Manufacturing Systems 70 (2023): 137-148.
  12. Cai, Zhengying, Xiangyu Du, Tianhao Huang, Tianrui Lv, Zhiheng Cai, and Guoqiang Gong. "Robotic Edge Intelligence for Energy-Efficient Human–Robot Collaboration." Sustainability (2071-1050) 16, no. 22 (2024).
  13. Taraglio, Sergio, Stefano Chiesa, Saverio De Vito, Marco Paoloni, Gabriele Piantadosi, Andrea Zanela, and Girolamo Di Francia. "Robots for the Energy Transition: A Review." Processes 12, no. 9 (2024): 1982.
  14. Farooq, Muhammad Umar, Amre Eizad, and Hyun-Ki Bae. "Power solutions for autonomous mobile robots: A survey." Robotics and Autonomous Systems 159 (2023): 104285.
  15. Choi, Minje, Seongjin Park, Ryujeong Lee, Sion Kim, Juhyeon Kwak, and Seungjae Lee. "Energy efficient robot operations by adaptive control schemes." Oxford Open Energy 3 (2024): oiae012.
  16. Forootan, Mohammad Mahdi, Iman Larki, Rahim Zahedi, and Abolfazl Ahmadi. "Machine learning and deep learning in energy systems: A review." Sustainability 14, no. 8 (2022): 4832.
  17. Khalili, Siavash, and Christian Breyer. "Review on 100% renewable energy system analyses—A bibliometric perspective." IEEE access 10 (2022): 125792-125834.
  18. Bhardwaj, Hitesh, Nabil Shaukat, Andrew Barber, Andy Blight, George Jackson-Mills, Andrew Pickering, Manman Yang et al. "Autonomous, Collaborative, and Confined Infrastructure Assessment with Purpose-Built Mega-Joey Robots." Robotics 14, no. 6 (2025): 80.
  19. Muqeet, Hafiz Abdul, Rehan Liaqat, Mohsin Jamil, and Asharf Ali Khan. "A state-of-the-art review of smart energy systems and their management in a smart grid environment." Energies 16, no. 1 (2023): 472.
  20. Soori, Mohsen, Behrooz Arezoo, and Roza Dastres. "Optimization of energy consumption in industrial robots, a review." Cognitive Robotics 3 (2023): 142-157.
  21. Prina, Matteo Giacomo, Benedetto Nastasi, Daniele Groppi, Steffi Misconel, Davide Astiaso Garcia, and Wolfram Sparber. "Comparison methods of energy system frameworks, models and scenario results." Renewable and Sustainable Energy Reviews 167 (2022): 112719