Honeywell and Carnegie Mellon University to collaborate on robotics and AI for connected DCs
The collaboration will focus on next-generation machine learning and robotic controls to help distribution centers address rising e-commerce demands and resource challenges.
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Honeywell has announced a strategic collaboration with Carnegie Mellon University to advance artificial intelligence and robotic technologies to help distribution centers address rising demands fueled by rapid growth in e-commerce.
The initiative brings together Honeywell Intelligrated, a division of Honeywell Safety and Productivity Solutions, and Carnegie Mellon’s National Robotics Engineering Center. The two organizations are advancing the capability of artificial intelligence and robotics technologies to benefit distribution centers, which are becoming more integrated and complex, and looking to robotics solutions to improve productivity and performance in fulfilling orders.
“It is becoming increasingly difficult to staff supply chain operations fast enough to satisfy the growth in e-commerce. Developing advanced machine learning capabilities and applying it to critical distribution center applications is a key enabler for our customers,” said Pieter Krynauw, president of Honeywell Intelligrated. “Our industry expertise coupled with the research capability of Carnegie Mellon accelerates our ability to bring advanced technology to market at scale and deliver much-needed capacity and productivity gains for distribution centers through digital transformation.”
“With this initiative, we are combining leading-edge robotic technology from Carnegie Mellon with Honeywell Intelligrated’s logistics and industrial robotics expertise,” said Herman Herman, director of the National Robotics Engineering Center at Carnegie Mellon University. “In a period of such extreme growth for robotics, it is vital to have the technical platform along with the domain expertise and real-world data to push technology forward to commercial maturity.”
The rapid growth of e-commerce is putting increased pressure on distribution centers to fulfill orders quickly and accurately for retailers and consumers. According to research by eMarketer, e-commerce sales in the U.S. will increase approximately 16 percent in 2018, to exceed $526 billion. This growth will continue, with e-commerce projected to account for more than 12 percent of U.S. retail sales in 2020.
The Honeywell and Carnegie Mellon University collaboration will focus on developing a next-generation architecture to control and operate multiple robotic applications. The platform uses machine learning to enable critical decision-making capabilities, intelligent motion, collision avoidance and reliable sensing making it practical to deploy advanced robotics in dynamic, unpredictable environments.
Honeywell’s approach enables continuous learning and performance improvements to adapt to changing conditions in the enterprise. This allows for the automation of more complex warehousing tasks, such as unloading shipping containers and picking packets or individual e-commerce orders.
About Honeywell Intelligrated
Honeywell Intelligrated delivers a broad portfolio of automation equipment, software, service and support to more than 60 of the top 100 global retailers and half of the top 100 internet retailers. The business has more than 25 years of experience providing robotics solutions for manufacturing and distribution operations and is a certified robot integrator by the Robotic Industries Association.
About the National Robotics Engineering Center
The National Robotics Engineering Center develops and matures robotics technologies and solutions from concept to commercialization, solving the most challenging problems in many sectors such as material handling, manufacturing, mining, agriculture, energy, defense and others. The organization has produced more than 650 inventions in over 20 years, with deep expertise in unmanned ground vehicles, autonomy, sensing and perception, machine learning, machine vision, operator assistance, 3D mapping and position estimation.
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