Correcting of Unexpected Localization Measurement for Indoor Automatic Mobile Robot Transportation Based on neural network

Abstract

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory automation. However, when mobile robots move in laboratory environments, mechanical errors, environmental disturbances, and signal interruptions are inevitable. This can compromise the accuracy of the robot's localization, which is crucial for the safety of staff, robots, and the laboratory. A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory environments. The proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile robots. The experimental results demonstrate the effectiveness of this proposed method.