Random Processes In The Parametric Control Of A Mobile Robot Based On Pattern Recognition Algorithms

Abstract

Mobile robots operating in unstructured real-world environments face inherent uncertainties from dynamic obstacles, variable terrains, and unpredictable events, which pose significant challenges to their control systems. This study focuses on the integration of random process theory and pattern recognition algorithms to optimize the parametric control of mobile robots. First, it systematically analyzes the probabilistic characteristics of discrete and continuous random variables, and explores typical discrete distribution laws (Bernoulli distribution, Binomial distribution, Poisson distribution) that underpin the modeling of stochastic phenomena in robotic systems. Second, it constructs a mathematical model for parametric control integrating pattern recognition, encompassing core components such as sensor data processing, state estimation (e.g., Kalman filtering), pattern recognition algorithms, and parameter control strategies. Finally, a case study of an indoor visual navigation system is presented, where an improved path-based algorithm achieves real-time image processing (≤40ms per frame) to meet the requirements of autonomous navigation. The research verifies that the synergy between random process theory and pattern recognition can enhance the adaptability and intelligence of mobile robots in uncertain environments, providing a theoretical and practical basis for the development of autonomous robotic systems.