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

Joonyeol Yang, Minhyeong Kang, Seulchan Lee and Sanghyun Kim ()
Additional contact information
Joonyeol Yang: Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of Korea
Minhyeong Kang: Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of Korea
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

Mathematics, 2025, vol. 13, issue 5, 1-20

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.

Keywords: autonomous navigation; traversability estimation; path planning; model predictive path integral (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/5/810/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/5/810/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:5:p:810-:d:1602962

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().