IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i5p810-d1602962.html
   My bibliography  Save this article

Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains

Author

Listed:
  • Joonyeol Yang

    (Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of Korea
    These authors contributed equally to this work.)

  • Minhyeong Kang

    (Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of Korea
    These authors contributed equally to this work.)

  • Seulchan Lee

    (Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of Korea)

  • Sanghyun Kim

    (Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of Korea)

Abstract

Navigating rough terrains requires a robust path planning algorithm that accounts for the physical properties of the environment to maintain stability and ensure safety. This article proposes the Hybrid A*-guided Model Predictive Path Integral (MPPI) algorithm augmented with traversability estimation to address the challenges of path planning on uneven terrains. The traversability estimation process quantifies surface characteristics, such as slope and roughness to identify traversable regions. Using this information, the Hybrid A* algorithm computes paths that minimize surface irregularities and prioritize regions with lower gradients, thereby enhancing stability and reducing dynamic disturbances. These computed paths are then used to define the mean control input for the MPPI algorithm, which performs localized optimization while adhering to the terrain-aware trajectory. By integrating terrain-aware guidance through the Hybrid A* algorithm with the MPPI, the proposed methodology automates the selection of the appropriate mean control input and enhances control performance by explicitly incorporating terrain properties into the planning process. Experimental results demonstrate the ability of the algorithm to navigate complex terrains with reduced roll and pitch motions, contributing to improved stability and performance.

Suggested Citation

  • Joonyeol Yang & Minhyeong Kang & Seulchan Lee & Sanghyun Kim, 2025. "Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains," Mathematics, MDPI, vol. 13(5), pages 1-20, February.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:810-:d:1602962
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/5/810/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/5/810/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jian Sun & Jie Zhao & Xiaoyang Hu & Hongwei Gao & Jiahui Yu, 2023. "Autonomous Navigation System of Indoor Mobile Robots Using 2D Lidar," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    2. Yanjie Liu & Chao Wang & Heng Wu & Yanlong Wei, 2023. "Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm," Mathematics, MDPI, vol. 11(21), pages 1-20, November.
    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. Alberto Marroquín & Gonzalo Garcia & Ernesto Fabregas & Ernesto Aranda-Escolástico & Gonzalo Farias, 2023. "Mobile Robot Navigation Based on Embedded Computer Vision," Mathematics, MDPI, vol. 11(11), pages 1-17, June.

    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:13:y:2025:i:5:p:810-:d:1602962. 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.