IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i3p641-d1328790.html
   My bibliography  Save this article

Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots

Author

Listed:
  • Agnieszka Sękala

    (Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland)

  • Tomasz Blaszczyk

    (IT Department, Zealand-University of Applied Sciences, Lyngvej 21, 4600 Køge, Denmark)

  • Krzysztof Foit

    (Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland)

  • Gabriel Kost

    (Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland)

Abstract

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company’s profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed.

Suggested Citation

  • Agnieszka Sękala & Tomasz Blaszczyk & Krzysztof Foit & Gabriel Kost, 2024. "Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots," Energies, MDPI, vol. 17(3), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:641-:d:1328790
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/3/641/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/3/641/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    2. Rabab Benotsmane & György Kovács, 2023. "Optimization of Energy Consumption of Industrial Robots Using Classical PID and MPC Controllers," Energies, MDPI, vol. 16(8), pages 1-28, April.
    3. Qianqian Guo & Zhifang Su, 2023. "The Application of Industrial Robot and the High-Quality Development of Manufacturing Industry: From a Sustainability Perspective," Sustainability, MDPI, vol. 15(16), pages 1-26, August.
    4. Hao Lv & Beibei Shi & Nan Li & Rong Kang, 2022. "Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Kai-Hua & Zhao, Yan-Xin & Jiang, Cui-Feng & Li, Zheng-Zheng, 2022. "Does green finance inspire sustainable development? Evidence from a global perspective," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 412-426.
    2. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    3. Luyang Tang & Bangke Lu & Tianhai Tian, 2023. "The Effect of Input Digitalization on Carbon Emission Intensity: An Empirical Analysis Based on China’s Manufacturing," IJERPH, MDPI, vol. 20(4), pages 1-22, February.
    4. Wang, Quan-Jing & Wang, Hai-Jie & Chang, Chun-Ping, 2022. "Environmental performance, green finance and green innovation: What's the long-run relationships among variables?," Energy Economics, Elsevier, vol. 110(C).
    5. Lee, Chien-Chiang & Wang, Chang-song, 2022. "Does natural resources matter for sustainable energy development in China: The role of technological progress," Resources Policy, Elsevier, vol. 79(C).
    6. Lee, Chi-Chuan & Zhang, Jian & Hou, Shanshuai, 2023. "The impact of regional renewable energy development on environmental sustainability in China," Resources Policy, Elsevier, vol. 80(C).
    7. Lee, Chi-Chuan & Lee, Chien-Chiang, 2022. "How does green finance affect green total factor productivity? Evidence from China," Energy Economics, Elsevier, vol. 107(C).
    8. Wen, Huwei & Liang, Weitao & Lee, Chien-Chiang, 2022. "Urban broadband infrastructure and green total-factor energy efficiency in China," Utilities Policy, Elsevier, vol. 79(C).
    9. Dongdong Lu & Zilong Wang, 2023. "Towards green economic recovery: how to improve green total factor productivity," Economic Change and Restructuring, Springer, vol. 56(5), pages 3163-3185, October.
    10. Wang, Jianlong & Wang, Weilong & Liu, Yong & Wu, Haitao, 2023. "Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China," Technology in Society, Elsevier, vol. 72(C).
    11. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    12. Liu, Xiaorui & Guo, Wen & Feng, Qiang & Wang, Peng, 2022. "Spatial correlation, driving factors and dynamic spatial spillover of electricity consumption in China: A perspective on industry heterogeneity," Energy, Elsevier, vol. 257(C).
    13. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    14. Zhou, Fengxiu & Wen, Huwei & Lee, Chien-Chiang, 2022. "Broadband infrastructure and export growth," Telecommunications Policy, Elsevier, vol. 46(5).
    15. Mingyue Chen & Shuting Wang & Xiaowen Wang, 2024. "How Does Artificial Intelligence Impact Green Development? Evidence from China," Sustainability, MDPI, vol. 16(3), pages 1-23, February.
    16. Zhao, Xin-Xin & Zheng, Mingbo & Fu, Qiang, 2022. "How natural disasters affect energy innovation? The perspective of environmental sustainability," Energy Economics, Elsevier, vol. 109(C).
    17. Oyku Yucel & Gizem Celik & Zafer Yilmaz, 2023. "Sustainable Investment Attitudes Based on Sustainable Finance Literacy and Perceived Environmental Impact," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    18. Qiong Shen & Yuxi Pan & Yanchao Feng, 2023. "The impacts of high-speed railway on environmental sustainability: quasi-experimental evidence from China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-19, December.
    19. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    20. Zhang, Qi-nan & Zhang, Fan-fan & Mai, Qiang, 2023. "Robot adoption and labor demand: A new interpretation from external competition," Technology in Society, Elsevier, vol. 74(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:641-:d:1328790. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.