Improving Navigation Precision of Autonomous Robots with Adaptive Monte Carlo Localization in Dynamic Scenarios

Autonomous navigation robots face substantial challenges in enhancing the accuracy and efficiency of movement localization, particularly in dynamic and unstructured environments. Localization accuracy is highly dependent on the methods employed, making continuous innovation essential to address obstacles in complex conditions. Various techniques, such as the Kalman filter, have been widely adopted to improve localization performance. However, while these methods can deliver satisfactory results under certain conditions, their effectiveness often declines in the presence of uncertainty—especially when dealing with rapid or unpredictable environmental changes. An alternative approach with greater adaptability is Adaptive Monte Carlo Localization (AMCL), which dynamically estimates the robot’s position while accounting for real-time environmental variations. Recent experiments comparing actual trajectories with those estimated using AMCL achieved accuracy levels of 89.2% on the x-axis and 91.2% on the y-axis, demonstrating the method’s effectiveness in improving robot localization precision.