Optimization of object classification and recognition for e-commerce logistics

Abstract

E-commerce, an online transaction in the information-based society, draws on various technologies to achieve automated order picking process for the fulfillment of supply chain's productivity. Robotic systems like Amazon Kiva are applied in logistics warehouses for low labor cost and high efficiency. Amazon Robotic Challenge (ARC) in 2017 aimed to explore a solution to bin picking problem in cluttered environment which is a common situation in logistics warehouses. Since the perception strategy is a key factor to picking performance, this thesis proposes a robust vision-based approach to object recognition for the robotic system of Team Nangyang in ARC. In this thesis, traditional methods and deep learning methods for object recognition are reviewed and verified. Five perception approaches based on GMS (Grid-based Motion Statistics), CNN (convolutional neural network) and image differencing are proposed to achieve the order picking. First the experiments of GMS + fixed sliding window, CNN + fixed sliding window and CNN + dynamic sliding window are designed and conducted. Then two hybrid methods which combine CNN + dynamic sliding window with GMS and image differencing are proposed and tested to get a more accurate suction point. Finally, after comparing all the experimental results, a conclusion is drawn that CNN + dynamic sliding window + image differencing is a robust perception method to realize the object recognition in unstructured workspace in logistics warehouses.