Software model makes 'transport' robots smarter

Software model makes ‘transport’ robots smarter

Teams of humans and robots collaborating will become more common in industries. For example, human and automated workers could work together to fulfill online orders by intelligently moving through a warehouse, selecting items to ship.

Researchers at the University of Missouri are bringing us closer to this reality by developing a software model intended to make “transport” robots smarter.

The research entitled “Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing” was published in the International Journal of Production Economics.

Optimize collaboration between humans and robots

Sharan Srinivas is an assistant professor with a cross-appointment in the Department of Industrial and Manufacturing Systems Engineering and the Department of Marketing.

“The robotic technology already exists,” Srinivas said. “Our goal is to make the best use of this technology through effective planning. To do this, we ask questions such as “given a list of items to pick, how do you optimize the route plan for both human and robot pickers?” or ‘how many items should a robot choose on a given visit? or ‘in what order should objects be collected for a given robotic visit?’ Likewise, we have a similar set of questions for the human worker. The hardest part is optimizing the collaboration plan between human pickers and robots. »

Much of the human effort and labor costs in this process comes from fulfilling online orders. Robotics companies try to optimize the process by developing collaborative robots, often called cobots or autonomous mobile robots (AMR). These robots can operate in different environments like a warehouse or a distribution center, and they are usually equipped with sensors and cameras that make navigation easier. The new model will result in faster fulfillment of customer orders by optimizing key decisions or questions regarding collaborative order picking.

“The robot is smart, so if it’s tasked with going to a particular location, it can navigate the warehouse and not hit any workers or other obstacles along the way,” Srinivas said.

Not a replacement for human workers

Srinivas specializes in data analysis and operational research. According to the professor, AMRs are not designed to replace human workers. Instead, they will work collaboratively to increase the efficiency of the order fulfillment process. For example, robots can help fulfill orders faster than a human worker. At the same time, human workers will still need to pick items from the shelves and place them on the robots, which will then transport them to a designated drop-off point inside the warehouse.

“The only downside is that these robots don’t have good gripping abilities,” Srinivas said. “But humans are good at grabbing objects, so we’re trying to leverage the strength of both resources – human workers and collaborative robots. So what happens in this case is that humans find themselves at different points in the warehouse, and instead of a worker walking across the entire aisle to pick up multiple items along the way, the robot will come to the human worker, and the human worker will pick up an item and put it on the robot. Therefore, the human worker will not have to force themselves to move large carts of heavy items throughout the warehouse.

Srinivas also says that a future application of the software could be applied to other places like grocery stores within three to five years. The robots could fulfill orders while maneuvering among the public.

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