Pose estimation is one of the most complicated and compromising problems for underground mining machine tracking, and it is particularly important for hydraulic support autonomous following mining machine (AFM) policy-making system. In this paper, a low-cost infrared vision-based system through an Efficient Perspective-n-Point (EPnP) algorithm is proposed. To improve efficiency and simplify computation, a traditional EPnP algorithm is modified through a nature-inspired heuristic optimization algorithm. The optimized algorithm is integrated into the AFM policy-making system to estimate the relative pose (R-Pose) estimation between hydraulic support and the mining machine’s shearer drum. Simple yet effective numerical simulations and industrial experiments were carried out to validate the proposed method. The pose estimation error was ≤1% under normal lighting and illuminance, and ≤2% in a simulated underground environment, which was accurate enough to meet the needs of practical applications. Both numerical simulation and industrial experiment proved the superiority of the approach.
The manipulation of multiple physical connected objects can be described by virtual connected string system. We proposed a Lyapunov-based stabilization control approach for virtual connected string with midway discontinue vertical force. The system is with Riemannian boundary. We use the backstepping method to transform the system into a stable system with Dirichlet boundary. In this way, we get the controller to make sure the closed-loop system converge to the zero point exponentially. Then, we construct a Lyapunov function to analyze the stability for the closed-loop system. We also show the system is well-posedness. Also, we use the active disturbance rejection control (ADRC) to reject the disturbance when disturbance is present. Some numerical stimulations show that the control law is the effective.
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