# Design and Development of an automated Robotic Pick & Stow System for an e-Commerce Warehouse

## TL;DR

This paper presents an integrated robotic pick-and-stow system for e-commerce warehouses, combining perception, planning, calibration, and gripping modules, demonstrated through simulations and real-world tests, with a focus on practical implementation.

## Contribution

The paper introduces a novel robotic system with a custom pneumatic gripper and integrated modules, developed for and tested in an e-commerce warehouse context, advancing automation capabilities.

## Key findings

- Achieved fifth place in Amazon Picking Challenge 2016
- Demonstrated effective object recognition with faster R-CNN
- Validated system performance through simulations and real-world experiments

## Abstract

In this paper, we provide details of a robotic system that can automate the task of picking and stowing objects from and to a rack in an e-commerce fulfillment warehouse. The system primarily comprises of four main modules: (1) Perception module responsible for recognizing query objects and localizing them in the 3-dimensional robot workspace; (2) Planning module generates necessary paths that the robot end- effector has to take for reaching the objects in the rack or in the tote; (3) Calibration module that defines the physical workspace for the robot visible through the on-board vision system; and (4) Gripping and suction system for picking and stowing different kinds of objects. The perception module uses a faster region-based Convolutional Neural Network (R-CNN) to recognize objects. We designed a novel two finger gripper that incorporates pneumatic valve based suction effect to enhance its ability to pick different kinds of objects. The system was developed by IITK-TCS team for participation in the Amazon Picking Challenge 2016 event. The team secured a fifth place in the stowing task in the event. The purpose of this article is to share our experiences with students and practicing engineers and enable them to build similar systems. The overall efficacy of the system is demonstrated through several simulation as well as real-world experiments with actual robots.