Ant behavior inspires more efficient warehouse robots
When it comes to groups that work together to get a job done, ants have pretty much got the process perfected. That’s why computer scientist Marco Dorigo studied the creatures’ behavior, and created his Ant Colony Optimization model – an algorithmic technique that can be applied to human endeavors, when efficiency is the order of the day. Scientists from Germany’s Fraunhofer Institute for Material Flow and Logistics have now applied these algorithms to a swarm of 50 autonomous shuttle robots working in a parts warehouse, in an effort to create a new and better type of materials-handling system.
The warehouse is actually a 1,000 square-meter (10,764 sq-ft) research facility, equipped to simulate a distribution center. It incorporates 600 small-parts bins located on storage shelves, and eight picking stations. The wheeled Multishuttle Moves robots are responsible for getting requested parts from the shelves, and delivering them to one of those stations as fast as possible.
Sick of Ads?
More than 700 New Atlas Plus subscribers read our newsletter and website without ads.
Join them for just US$19 a year.More Information
Besides having microprocessors running software based on the ant algorithms, the robots are also equipped with distance sensors, accelerometers, and laser scanners. All of the robots are informed when an order comes in, and proceed to check in with each other via a wireless local area network, to find out which one is closest to the bin containing that part. The task is then assigned to that robot (assuming it’s available), which uses a combination of its onboard navigation software and sensory input to determine the quickest route to and from the bin, and to avoid running into other robots.
Traditionally, many distribution warehouses utilize roller tracks in the floors for the transportation of goods. According to Fraunhofer, one of the advantages of its system is the fact that it’s more scalable – the floor can be left unaltered, with whatever number of robots being deployed as needed. The robots should also be quicker, as they can determine the fastest route for each situation, instead of being limited to following the rollers every time.