Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network  

作　　者：Jiahao Huang Steffen Jung inger Hui Liu Kerstin Thurow 

机构地区：[1]Center for Life Science Automation(CELISCA),University of Rostock,Rostock 18119,Germany [2]Institute of Automation,University of Rostock,Rostock 18119,Germany [3]Institute of Artificial Intelligence&Robotics(IAIR),School of Traffic&Transportation Engineering,Central South University,Changsha 410075,Hunan,China

出　　处：《Transportation Safety and Environment》2024年第2期24-35,共12页交通安全与环境（英文）

摘　　要：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.

关 键 词：mobile robots laboratory automation indoor localization neural network 

分 类 号：U279[机械工程—车辆工程]