Path Navigation and Precise Deviation Correction Control for Tracked Roadheaders in Confined Roadway Spaces of Underground Coal Mines

Aiming at the complex construction environment and autonomous navigation challenges in underground coal mine roadways, this paper proposes a path navigation and deviation correction control method for tracked roadheaders in confined roadway spaces. First, a two-dimensional planar grid model of the working scenario was constructed, with dimensionality reduction in the roadway model achieved through a heading reference influence degree threshold of the tracked roadheaders. Based on the kinematics theory of tracked roadheaders, kinematic and dynamic models for deviation correction in fully mechanized excavation roadways were established. Subsequently, a path planning and tracking correction algorithm was developed, along with a heading deviation correction control algorithm based on fuzzy neural network PID. Online optimization of the particle swarm algorithm was realized through crossover-mutation operations, enabling optimal strategy solving for construction path planning and precise control of travel deviation correction. Finally, simulation experiments evaluating algorithm performance and comparative simulations of control algorithms validated the feasibility and superiority of the proposed method. This research provides strategic guidance and theoretical foundations for rapid precision deployment and intelligent deviation correction control of tracked engineering vehicles in confined underground coal mine spaces.