{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T21:06:08Z","timestamp":1768597568573,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan program","award":["2023YFC2907405"],"award-info":[{"award-number":["2023YFC2907405"]}]},{"name":"Key Research and Development Program Project of Shanxi Province","award":["202402100101006"],"award-info":[{"award-number":["202402100101006"]}]},{"name":"Major Science and Technology Project of Shanxi Province","award":["202301150401011"],"award-info":[{"award-number":["202301150401011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>To achieve the automation and intelligence of mining equipment, it is essential to address the challenge of autonomous driving, with the core task being how to navigate safely from the starting point to the mining area endpoint. This paper proposes a boundary-aware multi-point preview control algorithm to tackle the strong dependency on predefined paths and the lack of foresight in the autonomous driving of underground articulated mining vehicles in highly confined underground spaces. The algorithm determines the driving direction by calculating the vehicle\u2019s real-time state and LiDAR data, previewing road conditions without relying on preset path planning. Experiments conducted in a ROS Noetic\/GAZEBO 11 simulation environment compared the proposed method with single-point and two-point preview algorithms, validating the effectiveness of the boundary-aware multi-point preview control. The results show that the proposed control strategy yields the lowest lateral deviation and the highest steering smoothness compared to single-point and two-point preview algorithms; it also outperforms the standard multi-point preview algorithm. This demonstrates its superior performance. Specifically, the proposed boundary-aware multi-point preview algorithm outperformed other methods in terms of steering smoothness and stability, significantly enhancing the vehicle system\u2019s adaptability, robustness, and safety.<\/jats:p>","DOI":"10.3390\/a19010076","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:28:12Z","timestamp":1768555692000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boundary-Aware Multi-Point Preview Control: An Algorithm for Autonomous Articulated Mining Vehicles Operating in Highly Constrained Underground Spaces"],"prefix":"10.3390","volume":"19","author":[{"given":"Shuo","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4806-636X","authenticated-orcid":false,"given":"Yiting","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2985-1071","authenticated-orcid":false,"given":"Jue","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Lv","sequence":"additional","affiliation":[{"name":"BGRIMM Intelligent Technology, Beijing 102628, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhu","sequence":"additional","affiliation":[{"name":"BGRIMM Intelligent Technology, Beijing 102628, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.eng.2018.05.013","article-title":"Intelligent Mining Technology for an Underground Metal Mine Based on Unmanned Equipment","volume":"4","author":"Li","year":"2018","journal-title":"Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1002\/rob.22185","article-title":"Autonomous Detection and Loading of Ore Piles with Load\u2013Haul\u2013Dump Machines in Room and Pillar Mines","volume":"40","author":"Cardenas","year":"2023","journal-title":"J. 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