Automated Container Terminal Oriented Travel Time Estimation of AGV

Abstract

Automated guided vehicles(AGV)are crucial for the horizontal transportation of automated container terminals.Accurate estimation of the travel time of each AGV will reduce the number of idle AGV resources and increase the efficiency of the entire terminal.This paper proposes a method for travel time estimation of AGV in automated container terminals.Firstly,the target route of AGV is divided and encoded into several segments.Secondly,other routes are encoded as environment information,which depart before or after the target route.And the conflict between these routes and target route is estimated as an auxiliary task.Finally,the travel time with all encodings is calculated.The proposed method introduces the influence of path conflicts on time estimation.Experiments based on historical data of automated terminals show that,compared with static time estimation methods commonly used in AGV scenarios,the proposed method can reduce the time estimation error by more than 18%,and can estimate the travel time more accurately.

Keywords

• automated container terminal|travel time estimation|agv|machine learning