ABSTRACT
This chapter introduces 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) A novel two finger pneumatic valve based 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. The system was developed by IITK-TCS team for participation in the Amazon Picking Challenge 2016 event. The overall efficacy of the system is demonstrated through several simulation as well as real-world experiments with actual robots.
