This research investigates the business case for using autonomous collaborative robots (“bots”) in warehouses. Bots are designed to work collaboratively with human workers in warehouses to fill orders, primarily in e-commerce environments. In order-picking applications, a bot knows the location of every item in the warehouse, and when an item needs to be picked for an order, the bot navigates to that item and waits. Human workers are assigned to patrol specific zones, and when they see a bot waiting, the worker walks to the bot, reads the instructions off the screen, executes the task, and moves on. The bot then drives to the next location, or to a packing or shipping station. Companies that have brought this technology to market advertise these bots as being very efficient systems because human walking is reduced; humans don’t have to carry anything or walk around the warehouse to fulfill an order. We investigate conditions where there is good application potential of bots and where there isn’t. Analytical models are developed for measuring the work content, productivity, and waiting time by the bot for a worker. A simulation model is built to validate the analytical models. We conclude that with the current pricing structure, the business case for bots is limited to operations with low pick density and throughput requirements that do not lead to excessive congestion or that would favor a more automated solution.
Progress in Material Handling Research
Meller, Russell D.; Nazzal, Dima; and Thomas, Lisa M., "Collaborative Bots in Distribution Centers" (2018). 15th IMHRC Proceedings (Savannah, Georgia. USA – 2018). 17.