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Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques

Author

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  • Sara Abdallaoui

    (DRIVE Laboratory EA1859, University of Bourgogne Franche Comté, F58000 Nevers, France)

  • El-Hassane Aglzim

    (DRIVE Laboratory EA1859, University of Bourgogne Franche Comté, F58000 Nevers, France)

  • Ahmed Chaibet

    (DRIVE Laboratory EA1859, University of Bourgogne Franche Comté, F58000 Nevers, France)

  • Ali Kribèche

    (DRIVE Laboratory EA1859, University of Bourgogne Franche Comté, F58000 Nevers, France)

Abstract

Mobile robot path planning has passed through multiple phases of development and took up several challenges. Up to now and with the new technology in hands, it becomes less complicated to conduct path planning for mobile robots and avoid both static and dynamic obstacles, so that collision-free navigation is ensured. Thorough state of the art review analysis with critical scrutiny of both safe and optimal paths for autonomous vehicles is addressed in this study. Emphasis is given to several developed techniques based using sampling algorithms, node-based optimal algorithms, mathematic model-based algorithms, bio-inspired algorithms, which includes neural network algorithms, and then multi-fusion-based algorithms, which combine different methods to overcome the drawbacks of each. All of these approaches consider different conditions and they are used for multiple domains.

Suggested Citation

  • Sara Abdallaoui & El-Hassane Aglzim & Ahmed Chaibet & Ali Kribèche, 2022. "Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques," Energies, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1358-:d:748677
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    References listed on IDEAS

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    4. Zexuan Zhu & Fangxiao Wang & Shan He & Yiwen Sun, 2015. "Global path planning of mobile robots using a memetic algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(11), pages 1982-1993, August.
    5. Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.
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    Cited by:

    1. Yue Li & Jianyou Zhao & Zenghua Chen & Gang Xiong & Sheng Liu, 2023. "A Robot Path Planning Method Based on Improved Genetic Algorithm and Improved Dynamic Window Approach," Sustainability, MDPI, vol. 15(5), pages 1-28, March.

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