ABB adds AI to robotic Item Picking family for fashion and logistics

ABB is expanding its portfolio of robotic solutions for logistics and e-commerce supply chains by adding two new AI-powered functional modules to its Item Picking family. Featuring ABB’s 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 9% per year^[1], 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% of global supply chain and logistics businesses experiencing significant workforce shortages^[2]. The expansion of our AI-powered item-picking family addresses these challenges. It 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. 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 using ABB’s AI-based vision technology. Both functional modules deliver industry-leading picking accuracy of over 99.5%, 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.

In addition to 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 polybagged 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 the AI vision system has identified each item.

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), various components, including robots, grippers, and cameras, can be easily integrated through a single computer and user interface. By addressing the key challenges of detection, grasping, and motion control using pre-trained AI models, vision, proven robotic performance, and an integrated control platform, the functional modules lower time and costs while eliminating R&D risks for system integrators in developing 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, setup, and operation.

For more information on how ABB’s Robotic Item Picking family can transform logistics operations through fast, flexible, and efficient performance, visit https://new.abb.com/products/robotics/functional-modules/item-picking-family

References:
^[1]McKinsey & Company – ‘Preparing post for further parcel opportunities‘ (2024)
^[2]Descartes – ‘How Bad Is The Supply Chain and Logistics Workforce Challenge?’. Findings based on 1,000 US and European supply chain and logistics companies (2024)