IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1210-d1043968.html
   My bibliography  Save this article

Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy

Author

Listed:
  • Piotr Szeląg

    (Faculty of Electrical Engineering, Częstochowa University of Technology, 17 Armii Krajowej Avenue, 42-201 Częstochowa, Poland)

  • Sebastian Dudzik

    (Faculty of Electrical Engineering, Częstochowa University of Technology, 17 Armii Krajowej Avenue, 42-201 Częstochowa, Poland)

  • Anna Podsiedlik

    (Faculty of Electrical Engineering, Częstochowa University of Technology, 17 Armii Krajowej Avenue, 42-201 Częstochowa, Poland)

Abstract

The task of controlling a wheeled mobile robot is an important element of navigation algorithms. The control algorithm manages the robot’s movement in accordance with the path determined by the planner module, where the accuracy of mapping the given route is very important. Most often, mobile robots are battery-powered, which makes minimizing energy consumption and shortening travel time an important issue. For this reason, in this work, the mobile robot control algorithm was tested in terms of energy consumption, travel time and path mapping accuracy. During the research, a criterion was developed, thanks to which it was possible to select the optimal parameters of the pure pursuit algorithm that controls the movement of the tested robot. The research was carried out in the Laboratory of Intelligent Mobile Robots using the QBot2e mobile robot operating on the basis of differential drive kinematics. As a result of the research, optimal values of the control parameters were obtained, minimizing the travel time, energy consumption and mapping error of the given paths.

Suggested Citation

  • Piotr Szeląg & Sebastian Dudzik & Anna Podsiedlik, 2023. "Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy," Energies, MDPI, vol. 16(3), pages 1-30, January.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1210-:d:1043968
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1210/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/3/1210/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sebastian Dudzik, 2020. "Application of the Motion Capture System to Estimate the Accuracy of a Wheeled Mobile Robot Localization," Energies, MDPI, vol. 13(23), pages 1-29, December.
    2. Kazuki Nonoyama & Ziang Liu & Tomofumi Fujiwara & Md Moktadir Alam & Tatsushi Nishi, 2022. "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.
    3. Dariusz Horla & Jacek Cieślak, 2020. "On Obtaining Energy-Optimal Trajectories for Landing of UAVs," Energies, MDPI, vol. 13(8), pages 1-25, April.
    4. Liu, Yang & Zhang, Qi & Lyu, Cheng & Liu, Zhiyuan, 2021. "Modelling the energy consumption of electric vehicles under uncertain and small data conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 313-328.
    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. Luiz Almeida & Ana Soares & Pedro Moura, 2023. "A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings," Energies, MDPI, vol. 16(13), pages 1-26, June.
    2. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    3. Md Moktadir Alam & Tatsushi Nishi & Ziang Liu & Tomofumi Fujiwara, 2023. "A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place," Energies, MDPI, vol. 16(19), pages 1-22, September.
    4. 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.
    5. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    6. Sun, Xilei & Fu, Jianqin, 2024. "Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle," Energy, Elsevier, vol. 300(C).
    7. Maciej Podsędkowski & Rafał Konopiński & Damian Obidowski & Katarzyna Koter, 2020. "Variable Pitch Propeller for UAV-Experimental Tests," Energies, MDPI, vol. 13(20), pages 1-16, October.
    8. Adam Rapalski & Sebastian Dudzik, 2023. "Energy Consumption Analysis of the Selected Navigation Algorithms for Wheeled Mobile Robots," Energies, MDPI, vol. 16(3), pages 1-37, February.
    9. Yanxiang Yang & Xiangyin Zhang & Jiayi Zhou & Bo Li & Kaiyu Qin, 2022. "Global Energy Consumption Optimization for UAV Swarm Topology Shaping," Energies, MDPI, vol. 15(7), pages 1-21, March.
    10. Kazuki Nonoyama & Ziang Liu & Tomofumi Fujiwara & Md Moktadir Alam & Tatsushi Nishi, 2022. "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.
    11. Tudorică, Bogdan-George & Bucur, Cristian & Panait, Mirela & Oprea, Simona-Vasilica & Bâra, Adela, 2024. "Energetic Equilibrium: Optimizing renewable and non-renewable energy sources via particle swarm optimization," Utilities Policy, Elsevier, vol. 87(C).
    12. Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    13. Liu, Yang & Wu, Fanyou & Lyu, Cheng & Li, Shen & Ye, Jieping & Qu, Xiaobo, 2022. "Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(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:16:y:2023:i:3:p:1210-:d:1043968. 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.