Adaptive Speed Estimation for Intelligent Auxiliary Transportation Robot on Complex Mining Roads

The rapid development of coal mining has raised new challenges to the safety and efficiency of traditional manual operations, accelerating the demand for intelligent Auxiliary transportation robots. Complex road conditions-characterized by variable surface adhesion and uncertain gradients-often cause wheel slip in wheeled robots, especially in four-wheel-drive systems. Such effects significantly reduce the accuracy of conventional vehicle speed estimation methods that rely on stable adhesion and fixed-slope assumptions. To address these challenges, this study proposes an adaptive extended Kalman filter-based speed estimation algorithm that simultaneously evaluates wheel-slip confidence using fuzzy logic and estimates road gradients in real time. The slip-rate-based fuzzy inference system adaptively adjusts the observation noise of each wheel, allowing the estimator to mitigate the adverse effects of slip. Meanwhile, the integrated gradient estimator compensates for the gravitational component in longitudinal acceleration measurements, improving estimation accuracy under varying slopes. Experiments conducted on roads with variable adhesion conditions show that the proposed method improves speed estimation accuracy by up to 20% compared with the traditional Extended Kalman Filter. Under variable-gradient conditions, the estimation error remains within approximately 5%, demonstrating strong adaptability to complex road profiles. The proposed approach provides a reliable foundation for intelligent transportation systems, enhancing operational safety and efficiency.