Safe Path Planning Method Based on Collision Prediction for Robotic Roadheader in Narrow Tunnels

Chao Zhang, Xuhui Zhang (), Wenjuan Yang, Guangming Zhang, Jicheng Wan, Mengyu Lei and Zheng Dong
Additional contact information
Chao Zhang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xuhui Zhang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Wenjuan Yang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Guangming Zhang: Faculty of Engineering and Technology, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
Jicheng Wan: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Mengyu Lei: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Zheng Dong: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Mathematics, 2025, vol. 13, issue 3, 1-26

Abstract: Safe path planning is essential for the autonomous operation of robotic roadheader in narrow underground tunnels, where limited perception and the robot’s geometric constraints present significant challenges. Traditional path planning methods often fail to address these issues. This paper proposes a collision prediction-integrated path planning method tailored for robotic roadheader in confined environments. The method comprises two components: collision prediction and path planning. A collision prediction model based on artificial potential fields is developed, considering the non-convex shape of the roadheader and enhancing scalability. By utilizing tunnel design information, a composite potential field model is created for both obstacles and the roadheader, enabling real-time collision forecasting. The A* algorithm is modified to incorporate the robot’s motion constraints, using a segmented weighted heuristic function based on collision predictions. Path smoothness is achieved through Bézier curve smoothing. Experimental results in both obstacle-free and obstacle-laden scenarios show that the proposed method outperforms traditional approaches in terms of computational efficiency, path length, and smoothness, ensuring safe, efficient navigation in narrow tunnels.

Keywords: robotic roadheader; path planning; collision prediction; narrow tunnel; artificial potential field model; improved A* (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/2227-7390/13/3/522/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/3/522/ (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:3:p:522-:d:1583815

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 ().