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Changqing Yan

Publications and source records attributed to Changqing Yan.

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MineRobot: An Actuator-Centered Kinematic Modeling and Solving Framework for Underground Mining Robots

Underground mining robots are increasingly modeled for planning, operator training, and digital-twin workflows, where reliable actuator-level kinematics is needed to reduce hazardous in situ trials. Unlike typical open-chain industrial manipulators, representative mining machines are often linear-actuator-driven closed-chain mechanisms with planar four-bar linkages, making reusable kinematic modeling and real-time FK/IK solving challenging. We present \textit{\hl{MineRobot}}, an actuator-centered framework for modeling and solving the kinematics of this representative mechanism class. MineRobot introduces the Mining Robot Description Format (MRDF), a domain-specific representation that parameterizes mining-robot kinematics with native semantics for actuators and loop closures. It then contracts planar four-bar substructures into generalized joints and extracts, for each actuator, an Independent Topologically Equivalent Path (ITEP) classified into four canonical types. Based on this decomposition, per-type solvers are composed into a sequential forward-kinematics (FK) pipeline, while inverse kinematics (IK) is formulated as a bound-constrained actuator-length optimization solved by a Gauss--Seidel-style update scheme. By converting coupled closed-chain kinematics into small topology-aware solves, MineRobot reduces robot-specific hand derivations and supports efficient repeated FK/IK computation without treating each query as a full coupled constraint-solving problem. Experiments on representative underground mining robots demonstrate real-time FK performance and robust IK convergence within the tested operating ranges, supporting the use of MineRobot as an actuator-centered kinematic layer for planning, training, and digital-twin workflows.

cs.GR

Traffic Flow Simulation for Autonomous Driving

A traffic system is a random and complex large system, which is difficult to conduct repeated modelling and control research in a real traffic environment. With the development of automatic driving technology, the requirements for testing and evaluating the development of automatic driving technology are getting higher and higher, so the application of computer technology for traffic simulation has become a very effective technical means. Based on the micro-traffic flow modelling, this paper adopts the vehicle motion model based on cellular automata and the theory of bicycle intelligence to build the simulation environment of autonomous vehicle flow. The architecture of autonomous vehicles is generally divided into a perception system, decision system and control system. The perception system is generally divided into many subsystems, responsible for autonomous vehicle positioning, obstacle recognition, traffic signal detection and recognition and other tasks. Decision systems are typically divided into many subsystems that are responsible for tasks such as path planning, path planning, behavior selection, motion planning, and control. The control system is the basis of the selfdriving car, and each control system of the vehicle needs to be connected with the decision-making system through the bus, and can accurately control the acceleration degree, braking degree, steering amplitude, lighting control and other driving actions according to the bus instructions issued by the decision-making system, so as to achieve the autonomous driving of the vehicle.

cs.RO