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.

翻译：地下采矿机器人在虚拟环境中越来越多地用于训练、规划及数字孪生应用，其中可靠的运动学对于避免危险现场试验至关重要。与典型开链工业机械臂不同，采矿机器人通常是由线性致动器驱动且包含平面四杆机构的闭链机构，这使得运动学建模与实时求解均为挑战。我们提出MineRobot——一个用于虚拟环境中地下采矿机器人运动学建模与求解的统一框架。首先，我们引入采矿机器人描述格式（MRDF），这是一种领域特定表示方法，通过致动器与环路闭合的原生语义对采矿机器人运动学进行参数化。其次，我们开发了拓扑处理流程，将四杆子结构收缩为广义关节，并为每个致动器提取独立拓扑等效路径（ITEP），该路径被归为四种规范类型之一。第三，利用ITEP的独立性，我们构建了基于类型的求解器，并将其组合为以致动器为中心的序列化正向运动学（FK）流水线。基于相同分解，我们将逆向运动学（IK）表述为带界约束的优化问题，并通过交替更新致动器长度的Gauss-Seidel式流程进行求解。通过将耦合的闭环运动学转换为一系列小规模拓扑感知求解，该框架避免了机器人特定的手动推导并支持高效计算。实验表明，MineRobot能够提供虚拟环境应用所需的实时性能与鲁棒性。