Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains
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 estimatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/5/810 |
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| author | Joonyeol Yang Minhyeong Kang Seulchan Lee Sanghyun Kim |
| author_facet | Joonyeol Yang Minhyeong Kang Seulchan Lee Sanghyun Kim |
| author_sort | Joonyeol Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b3f8ff3f467a476cb8c03ad24db65e9d |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-b3f8ff3f467a476cb8c03ad24db65e9d2025-08-20T02:53:02ZengMDPI AGMathematics2227-73902025-02-0113581010.3390/math13050810Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough TerrainsJoonyeol Yang 0Minhyeong Kang 1Seulchan Lee2Sanghyun Kim3Department of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of KoreaDepartment of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of KoreaDepartment of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of KoreaDepartment of Mechanical Engineering, Kyung Hee University, 1732 Deogyeong-daero Giheung-gu, Yongin-si 17104, Gyeonggi-Do, Republic of KoreaNavigating 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.https://www.mdpi.com/2227-7390/13/5/810autonomous navigationtraversability estimationpath planningmodel predictive path integral |
| spellingShingle | Joonyeol Yang Minhyeong Kang Seulchan Lee Sanghyun Kim Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains Mathematics autonomous navigation traversability estimation path planning model predictive path integral |
| title | Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains |
| title_full | Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains |
| title_fullStr | Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains |
| title_full_unstemmed | Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains |
| title_short | Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains |
| title_sort | hybrid a guided model predictive path integral control for robust navigation in rough terrains |
| topic | autonomous navigation traversability estimation path planning model predictive path integral |
| url | https://www.mdpi.com/2227-7390/13/5/810 |
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