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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
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    References listed on IDEAS

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    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.
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    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).

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