Warehouse Automation through Drones, IoT, and Machine Learning
Abstract
Warehouse automation plays a significant role in efficient and effective storage and retrieval of objects, while also ensuring that these objects are physically handled with care and remain under ambient conditions that are deemed appropriate. While manual warehouse processes can be used to accomplish the same, automation ensures consistency in associated processes, with improved performance of the entire system. For example, automation can speed up processes such as pick-and-place operations and at the same time experience relatively few (human-introduced) errors such as those that are observed in manual systems. We consider the use of drones and Internet of Things (IoT) devices such as RFID (Radio-Frequency Identification) tags to automate warehouse operations. The RFID tags are used to uniquely identify and locate objects in the warehouse and to measure the object’s ambient conditions through on-board sensors when appropriate and necessary. The drones are used to locate objects and their locations, as well as help with pick-and-place operations. We use machine learning to learn the patterns in these systems to automate associated processes.
Short Biography
Selwyn Piramuthu is Professor of Information Systems and Operations Management at the University of Florida, where he has taught since Fall 1991. Trained in machine learning, his research interests also include cryptography with applications related to IoT/RFID, privacy/security, supply chain management, among others. His (co-authored with Wei Zhou) book titled, “RFID and Sensor Network Automation in the Food Industry” was published by Wiley in 2016. He received his B.Tech., M.S., and Ph.D. respectively from IIT-Madras, University of Arizona, and University of Illinois at Urbana-Champaign.
