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ABB expands robotic Item Picking Family with new AI-powered functional modules

ABB expands robotic Item Picking Family with new AI-powered functional modules



ABB is expanding its portfolio of robotic solutions for logistics and e-commerce supply chains with the addition of two new AI-powered functional modules to its Item Picking family. Featuring ABB’s own AI-based vision technology, tested by world leading fashion retail and logistics companies, the Fashion Inductor and Parcel Inductor offer solutions for two of the most critical logistics processes: item picking and sorter induction.

“With the growth of e-commerce set to drive increased parcel volumes of up to nine percent per year, there is a need for greater levels of speed and accuracy in picking and inducting unknown random items,” said Craig McDonnell, Managing Director Business Line Industries for ABB Robotics. “At the same time, companies are finding it harder to recruit people to perform these repetitive and non-ergonomic tasks, with 37 percent of global supply chain and logistics businesses experiencing significant workforce shortages. The expansion of our AI-powered item picking family addresses these challenges and enables companies to increase throughput and productivity, while reducing errors through the end-to-end automation of their processes.”

By making it possible to handle items at higher accuracy and speed levels, the functional modules address the problems of picking and inducting through mixes of packages and other items in warehouses and parcel sorting depots. Using ABB´s AI-based vision technology, both the Robotic Fashion Inductor and Robotic Parcel Inductor can handle unknown and randomly arranged items in unstructured environments, ensuring seamless processing in high-throughput, high-mix logistics operations. Both functional modules deliver industry-leading picking accuracy of over 99.5 percent, even in highly dynamic environments where item sizes, shapes, and packaging types vary daily. The AI system can also be trained to detect and reject non-inducible items, such as spherical or cylindrical products, ensuring reliable and efficient operations.

As well as order processing, the ability to handle random and unexpected objects is also beneficial for returned packages that cannot be predicted by time, type or date.

The Robotic Fashion Inductor enables singulation and sorter induction for poly-bagged apparel and accessories items at speeds of up to 1,300 picks per hour. The Robotic Parcel Inductor is built for small parcel singulation and sorter induction, processing boxes, bags, envelopes, and packages at up to 1,500 picks per hour in post and parcel logistics centers. Integration with ABB’s motion planning software enables collision-free automatic path planning once each item has been identified by the AI vision system.

Designed for rapid deployment, both functional modules come pre-integrated, minimizing commissioning time, operational errors, and setup complexity, with commissioning possible in as little as one week. Using ABB’s Application Controller Platform (ACP), different components including robots, grippers, and cameras can be seamlessly integrated through a single computer and user interface. By solving the core challenges of detection, grasping, and motion control through pre-trained AI models, vision, proven robotic performance, and an integrated control platform, the functional modules reduce time and costs and eliminate R&D risks for system integrators in building automated picking solutions.

As a single-source supplier, ABB provides end-to-end support, from dedicated application software and robotic hardware to global after-sales service, ensuring seamless implementation and ongoing reliability. This approach eliminates the issues associated with sourcing and integrating solutions from multiple suppliers, enabling easier specification, set up and operation.

For more information on how ABB’s Robotic Item Picking family can transform logistics operations through fast, flexible and efficient performance, click here.



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