IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i12p1787-d1411053.html
   My bibliography  Save this article

Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency

Author

Listed:
  • Safa Jameel Al-Kamil

    (Doctoral School on Safety and Security Sciences, Óbuda University, 1034 Budapest, Hungary)

  • Róbert Szabolcsi

    (Institute of Mechatronics and Vehicle Engineering, Óbuda University, 1081 Budapest, Hungary)

Abstract

Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.

Suggested Citation

  • Safa Jameel Al-Kamil & Róbert Szabolcsi, 2024. "Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency," Mathematics, MDPI, vol. 12(12), pages 1-21, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1787-:d:1411053
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/12/1787/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/12/1787/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ajay Bandi & Pydi Venkata Satya Ramesh Adapa & Yudu Eswar Vinay Pratap Kumar Kuchi, 2023. "The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges," Future Internet, MDPI, vol. 15(8), pages 1-60, July.
    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. Nikša Alfirević & Daniela Garbin Praničević & Mirela Mabić, 2024. "Custom-Trained Large Language Models as Open Educational Resources: An Exploratory Research of a Business Management Educational Chatbot in Croatia and Bosnia and Herzegovina," Sustainability, MDPI, vol. 16(12), pages 1-18, June.
    2. Shaotong Qi & Yubo Cheng & Zhiyuan Li & Jiaxin Wang & Huaiyi Li & Chunwei Zhang, 2024. "Advanced Deep Learning Techniques for Battery Thermal Management in New Energy Vehicles," Energies, MDPI, vol. 17(16), pages 1-38, August.
    3. Theodora Sanida & Maria Vasiliki Sanida & Argyrios Sideris & Minas Dasygenis, 2024. "Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques," J, MDPI, vol. 7(3), pages 1-17, August.

    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:jmathe:v:12:y:2024:i:12:p:1787-:d:1411053. 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.