An Innovative Robotics Stowing Strategy For Inventory Replenishment In Automated Storage And Retrieval System

Modern automated warehouses are equipped with one or many expensive and sophisticated equipment, such as palletizing robots, automated guided vehicles as well as an automated storage and retrieval system (AS/RS). These equipment are operated manually at many levels. These manual interruptions are accompanied by disadvantages of slow storage and retrieval speed, high operating costs and high frequency of errors in the operations. This paper presents an approach for efficient robotic stowing of items for inventory replenishment in a storage system. The objective is to enable a robotic arm system to stow items into a storage bin system and automatically generate a file to indicate which bin each object is stowed to. This would require a robust object recognition imbued with recognition history such that a previously recognized object is remembered as being stowed, even if it has been obscured by other objects subsequently during the task. A feature confidence aggregation strategy has been implemented to analyze a sequence of images containing a number of objects that are added to the storage system sequentially. The strategy is based on a weighted aggregation of ranked machine-learned classification scores and feature-matching recognition scores. This method is able to produce a high recognition rate and has been applied in the Amazon Robotic Challenge 2017 by Team Nanyang.