MineRobot: A Unified Framework for Kinematics Modeling and Solving of Underground Mining Robots in Virtual Environments
Shengzhe Hou, Xinming Lu, Tianyu Zhang, Changqing Yan, Xingli Zhang
- Year
- 2026
- Access
- Open access
Abstract
Underground mining robots are increasingly operated in virtual environments (VEs) for training, planning, and digital-twin applications, where reliable kinematics is essential for avoiding hazardous in-situ trials. Unlike typical open-chain industrial manipulators, mining robots are often closed-chain mechanisms driven by linear actuators and involving planar four-bar linkages, which makes both kinematics modeling and real-time solving challenging. We present \emph{MineRobot}, a unified framework for modeling and solving the kinematics of underground mining robots in VEs. First, we introduce the Mining Robot Description Format (MRDF), a domain-specific representation that parameterizes kinematics for mining robots with native semantics for actuators and loop closures. Second, we develop a topology-processing pipeline that contracts four-bar substructures into generalized joints and, for each actuator, extracts an Independent Topologically Equivalent Path (ITEP), which is classified into one of four canonical types. Third, leveraging ITEP independence, we compose per-type solvers into an actuator-centered sequential forward-kinematics (FK) pipeline. Building on the same decomposition, we formulate inverse kinematics (IK) as a bound-constrained optimization problem and solve it with a Gauss--Seidel-style procedure that alternates actuator-length updates. By converting coupled closed-loop kinematics into a sequence of small topology-aware solves, the framework avoids robot-specific hand derivations and supports efficient computation. Experiments demonstrate that MineRobot provides the real-time performance and robustness required by VE applications.
Keywords
Related papers
State-of-the-art in mobile robot-assisted grinding technologies for large-scale complex components
Yusen Li, Ziwei Wang, Xiangye Zhu +9 more
Robotics and Computer-Integrated Manufacturing · 2026
A fusion prediction model of tool wear based on physical information and machine learning in five-axis milling TC4 titanium alloy
Shaoqing Qin, Lida Zhu, Yanpeng Hao +7 more
Robotics and Computer-Integrated Manufacturing · 2026
Enhancing robotic milling quality via a novel piezoelectric active damping toolholder
Bo Li, Yuanbo Zhao, Huijie Xiao +3 more
Robotics and Computer-Integrated Manufacturing · 2026
A novel method of suppressing low-frequency chatter in robotic milling using magnetically-induced nonlinear broadband multidirectional passive vibration absorber
Hao Li, Yuhui Yu, Rui Fu +3 more
Robotics and Computer-Integrated Manufacturing · 2026