Tohoku University and Sharp Corporation have begun joint research on simultaneous control of multiple automated transport robots using quantum annealing. We are working on the development of a high-speed computer that can instantly calculate the optimal route for 1000 automated transport robots in a distribution warehouse.
In the logistics industry, in addition to an increase in handling volume and product types due to the expansion of EC (electronic commerce), a labor shortage is expected due to the "2024 problem" (regulations on overtime work caps). The need for labor savings and efficiency improvements is rapidly increasing.
Therefore, the two parties have now agreed to jointly develop technology to dramatically improve productivity in logistics warehouses, and will conduct demonstration research applying Tohoku University's quantum annealing technology to Sharp's automatic transport robot control system. .
Quantum annealing is a computational technique that derives solutions from a huge number of combinations. D-Wave Systems' quantum annealing machine realizes this technology, but the current number of qubits, calculations, and communication environment alone are not suitable for services such as real-time optimization in the real world.
Therefore, the joint research will develop a dedicated high-speed computer based on unique algorithms and calculation processes specific to control systems. This computer can perform calculations hundreds to thousands of times faster than normal processing by a general-purpose computer, and can instantly calculate the optimal route for 1000 automated transport robots.
As the number of products handled becomes larger and more diverse, and robot operations in warehouses become more complex, we aim to realize large-scale simultaneous control of automated transport robots in large-scale warehouses. The system can also be applied to things such as picking order, product placement, and overall warehouse layout design, greatly improving warehouse operational efficiency. The company aims to carry out demonstration experiments using a prototype in 2024, and to put it into practical use in 2025.
