IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i3p1233-d486531.html
   My bibliography  Save this article

Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

Author

Listed:
  • Ángel Valera

    (Instituto de Automática e Informática Industrial (ai2), Universitat Politècnica de València, 46022 Valencia, Spain)

  • Francisco Valero

    (Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Marina Vallés

    (Instituto de Automática e Informática Industrial (ai2), Universitat Politècnica de València, 46022 Valencia, Spain)

  • Antonio Besa

    (Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Vicente Mata

    (Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Carlos Llopis-Albert

    (Center of Technological Research in Mechanical Engineering, Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle’s dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.

Suggested Citation

  • Ángel Valera & Francisco Valero & Marina Vallés & Antonio Besa & Vicente Mata & Carlos Llopis-Albert, 2021. "Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1233-:d:486531
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/3/1233/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/3/1233/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francisco Valero & Francisco Rubio & Carlos Llopis-Albert & Juan Ignacio Cuadrado, 2017. "Influence of the Friction Coefficient on the Trajectory Performance for a Car-Like Robot," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, June.
    2. Tang, J.F. & Mu, L.F. & Kwong, C.K. & Luo, X.G., 2011. "An optimization model for software component selection under multiple applications development," European Journal of Operational Research, Elsevier, vol. 212(2), pages 301-311, July.
    3. Xiaoyan Yu & Marin Marinov, 2020. "A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles," Sustainability, MDPI, vol. 12(8), pages 1-26, April.
    4. Rubio, Francisco & Llopis-Albert, Carlos & Valero, Francisco & Besa, Antonio José, 2020. "Sustainability and optimization in the automotive sector for adaptation to government vehicle pollutant emission regulations," Journal of Business Research, Elsevier, vol. 112(C), pages 561-566.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Llopis-Albert, Carlos & Palacios-Marqués, Daniel & Simón-Moya, Virginia, 2021. "Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicle," Technological Forecasting and Social Change, Elsevier, vol. 169(C).

    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. Secinaro, Silvana & Calandra, Davide & Lanzalonga, Federico & Ferraris, Alberto, 2022. "Electric vehicles’ consumer behaviours: Mapping the field and providing a research agenda," Journal of Business Research, Elsevier, vol. 150(C), pages 399-416.
    2. Foroughi, Behzad & Nhan, Pham Viet & Iranmanesh, Mohammad & Ghobakhloo, Morteza & Nilashi, Mehrbakhsh & Yadegaridehkordi, Elaheh, 2023. "Determinants of intention to use autonomous vehicles: Findings from PLS-SEM and ANFIS," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    3. Rubio, Francisco & Llopis-Albert, Carlos & Besa, Antonio José, 2023. "Optimal allocation of energy sources in hydrogen production for sustainable deployment of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    4. Liu, Yang Stephanie & Zhou, Xiaoyan & Yang, Jessica Hong & Hoepner, Andreas G.F. & Kakabadse, Nada, 2023. "Carbon emissions, carbon disclosure and organizational performance," International Review of Financial Analysis, Elsevier, vol. 90(C).
    5. Eslami, Hossein & Krishnan, Trichy, 2023. "New sustainable product adoption: The role of economic and social factors," Energy Policy, Elsevier, vol. 183(C).
    6. Rashidi-Sabet, Siavash & Madhavaram, Sreedhar & Parvatiyar, Atul, 2022. "Strategic solutions for the climate change social dilemma: An integrative taxonomy, a systematic review, and research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 619-635.
    7. Karol Tucki, 2021. "A Computer Tool for Modelling CO 2 Emissions in Driving Tests for Vehicles with Diesel Engines," Energies, MDPI, vol. 14(2), pages 1-30, January.
    8. Lifeng Mu & Vijayan Sugumaran & Fangyuan Wang, 2020. "A Hybrid Genetic Algorithm for Software Architecture Re-Modularization," Information Systems Frontiers, Springer, vol. 22(5), pages 1133-1161, October.
    9. Llopis-Albert, Carlos & Palacios-Marqués, Daniel & Simón-Moya, Virginia, 2021. "Fuzzy set qualitative comparative analysis (fsQCA) applied to the adaptation of the automobile industry to meet the emission standards of climate change policies via the deployment of electric vehicle," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    10. Preechaya Chavalittumrong & Mark Speece, 2022. "Three-Pillar Sustainability and Brand Image: A Qualitative Investigation in Thailand’s Household Durables Industry," Sustainability, MDPI, vol. 14(18), pages 1-22, September.
    11. Yongkyu Choi & Keun Tae Cho, 2021. "Analysis of Environmental Management Characteristics Using Network Analysis of CEO Communication in the Automotive Industry," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    12. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    13. Die Hu & Lu Qiu & Maoyan She & Yu Wang, 2021. "Sustaining the sustainable development: How do firms turn government green subsidies into financial performance through green innovation?," Business Strategy and the Environment, Wiley Blackwell, vol. 30(5), pages 2271-2292, July.
    14. Stoicho Stoev, 2019. "Using of Additional Packages of Components for Accelerated Application Development," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 8(2), pages 171-179, August.
    15. Fairouz Mustafa & Suman Lodh & Monomita Nandy & Vikas Kumar, 2022. "Coupling of cryptocurrency trading with the sustainable environmental goals: Is it on the cards?," Business Strategy and the Environment, Wiley Blackwell, vol. 31(3), pages 1152-1168, March.
    16. Łukasz Warguła & Piotr Kaczmarzyk, 2022. "Legal Regulations of Restrictions of Air Pollution Made by Mobile Positive Pressure Fans—The Case Study for Europe: A Review," Energies, MDPI, vol. 15(20), pages 1-11, October.
    17. Pradeep Kumar & Shailendra Narayan Singh & Sudhir Dawra, 2022. "Software component reusability prediction using extra tree classifier and enhanced Harris hawks optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 892-903, April.
    18. Yao-Liang Chung, 2023. "Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes," Future Internet, MDPI, vol. 15(10), pages 1-26, September.
    19. Lau, Kwok Hung, 2013. "Measuring distribution efficiency of a retail network through data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 146(2), pages 598-611.
    20. Shilpi Verma & Mukesh Kumar Mehlawat & Divya Mahajan, 2022. "Software component evaluation and selection using TOPSIS and fuzzy interactive approach under multiple applications development," Annals of Operations Research, Springer, vol. 312(1), pages 441-471, May.

    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:jsusta:v:13:y:2021:i:3:p:1233-:d:486531. 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.