A Practical Cobot Use Case: Solving the Travel Time Problem in Warehouses and Distribution Centers
I like to use real world examples to illustrate the technology about which I write, and especially for technology around which swirl clouds of hype and fantasy. When it comes to cobots there’s no lack of these. It was therefore refreshing to read a report issued by The Association for Advancing Automation, which provided a new benchmark for the adoption of cobots:
New A3 Report Signals Steady Automation Investment in First Half of 2025
“Collaborative Robots Show Rising Influence
Cobots (collaborative robots) accounted for a growing share of the market with 3,085 units ordered in the first half of 2025, valued at $114 million. In Q2 alone, cobots made up 23.7% of all units and 14.7% of revenue. These systems are increasingly favored for their ability to work safely alongside humans and address automation needs in space- or labor-constrained environments. A3 began tracking cobots as a distinct category in 1Q 2025 and plans to expand future reporting to include growth trends by sector.”
My area of expertise being process optimization in warehouses and distribution centers, I can confirm both space and labor constraints apply to DCs.
The use case presented here is one I have personally helped develop and, given where the state of the art presently exists for human and robot interactions, is practical.
Let’s first understand the problem this cobot use case was developed to solve. Simply put, how can travel time be dramatically reduced for human order selectors, thereby increasing their productivity.
Since the dawn of distribution, order selection for the items stored in a warehouse, and later, in distribution centers has included a certain amount of travel time that a human needed to do, and the more travel time was needed, the less productive the human was.
How is productivity measured in order selection?
Traditionally, order selection productivity came down to how many items a human could correctly select from storage locations in an hour. The longer the human had to travel from location to location in the pick path, then to drop off a pallet or cart at a packing area, get a new pallet or cart, and return to a starting point in the storage area, reduced the hourly productivity.
Travel time is the enemy of productivity.
Efforts to make humans more productive have involved technologies such as RF (radio-frequency) scanning and voice-directed workflows, typically used together in a multimodal solution. While these technologies have improved the location-to-location productivity of humans, they have not definitively solved for travel time.
A significant reason for this has been older warehouses and distribution centers are not optimized to reduce travel time, especially from the picking locations to the drop-off staging areas. Changing the infrastructure within these buildings is costly. While new warehouses and distribution centers (green fields) can be designed to optimize order selection, there are still a lot of older warehouses and distribution centers in use today.
A traditional solution to this problem has been to deploy additional humans as movers of pallets and carts from the order selection locations to packing and staging, allowing order selectors (pickers) to stay in their areas to focus on starting the next assignment without the travel time delay. Of course, while order selection productivity increased, so did the cost of overall order fulfillment due to the additional cost of the movers.
The cobot use case I helped develop took advantage of some basic factors: The costs of robots (in this case autonomous mobile robots – AMRs) was dropping and becoming favorable to human labor costs, and the cost of the physical modification of the distribution center was financially and operationally prohibitive.
The cobot solution was, at a high level, straightforward: Use AMRs to move pallets from the order selection areas to packing and replenish empty pallets at strategic locations within the order selection area.
Remember that old saying: “God (or the Devil) is in the details.”
Well, in this case it was the Devil. Some of the details that had to be considered included the particularly important step of alerting an AMR that a picker had dropped a full pallet at a strategic pickup location.
When picking items to a pallet, typically a unique barcoded label is affixed to the pallet. The barcode identifies the pallet as belonging to a selection assignment for a particular customer, and the position of the pallet if there are more than one for the assignment (e.g., 2 of 3).
The human order selector (picker), augmented with multimodal technologies (voice-direction plus RF barcode ring scanner), associates the pallet label with his or her assignment before picking the first item to the pallet. When picking to that pallet is completed, it is dropped at a strategic location (usually at the end of the aisle in which the last item on the pallet was picked). This location also has a barcode.
The cobot solution integrated the systemic drop notification at the strategic location from the augmented human’s multimodal solution with the AMR’s (autonomous mobile robot) operating system. This system could determine which AMR was closest to the drop that could make the pickup. The AMR would move to that location, read the location label to verify it was at the right place, and if so, would then read the pallet label.
The AMR system would know the pallet should be moved to a particular packing or staging location that was preassigned to handle and prepare the customer’s order, to which the pallet label referred. While the AMR was moving the full pallet, the augmented human would have already received and started his or her next picking assignment without leaving the picking locations. Travel time was eliminated and order selection productivity dramatically improved.
This kind of cobot use case will see increasing adoption as the rise of cobots continues.
About the Author
Tim Lindner develops multimodal technology solutions (voice / augmented reality / RF scanning) that focus on meeting or exceeding logistics and supply chain customers’ productivity improvement objectives. He can be reached at linkedin.com/in/timlindner.
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