At Simcona Electronics’ 50,000-square-foot facility, industrial robots participate in field experiments. These robots are part of Rochester Institute of Technology’s research on a materials-handling system for warehouses that combines technologies including artificial intelligence and light detection and ranging, or LiDAR, sensors.
“We needed the real setting to be able to do this work and to move it forward. They provide an extremely valuable resource for us,” says Michael Kuhl, professor of industrial and systems engineering at RIT’s Kate Gleason College of Engineering, of the Rochester company.
Kuhl and his team are working on “effective and efficient driving for material handling,” a one-year, $300,000 project sponsored by the Raymond Corp. It builds on earlier research with the company that established task selection and path planning of individual autonomous mobile robots, officials say.
New work focuses on advanced avoidance and communication strategies for multiple robots and humans in the warehouse environment. Increased demand for e-commerce coupled with supply chain hurdles have opened the doors to technology for help with productivity and safety in warehouses.
“This is one area where robotics and autonomous material handling can help,” Kuhl says. “Robots can work longer periods of time—not necessarily to replace jobs, but on some of the manual, non-value-added tasks. It means a change of focus of jobs, with people needed to design and maintain fleets of vehicles and robots.”
Most warehouses house human-operated and autonomous equipment. Avoidance strategies need to be integrated with task options, path planning, and recognition of multiple robots able to communicate with one another in real time, as well as recognize humans who work in the space.
“We have information about localization, the different types of sensors that we use within the warehouse to try to identify where the robots are located, and the actual movement of the robot,” Kuhl says. “Can they plan to get from the current location to destination safely and efficiently? They can have a short path, but they still need to avoid other robots and people.”
Machine learning techniques—deep neural networks—help train system components to make specific, sequenced decisions based on common tasks. They are also trained on infrequent or unusual actions that might occur.
Kuhl’s team is examining WiFi and cellular network technology functions in the warehouse environment as viable solutions. New standards for cellular technologies permit increased individual cellular communication between individual devices, Kuhl says.
“In terms of people and vehicles interacting, could we take advantage of the sensors of multiple vehicles moving around the warehouse?” he says. “If a vehicle is coming down one path, and it sees a person or another vehicle coming out of an aisle, can they communicate and make a decision about what to do next? Who has the right of way?”
The team has found that robots will be able to react.
A majority of distribution centers and warehouses are operated manually. Industry experts, however, expect robotic warehouse offerings to grow in the future as consumers continue to shop online.
The warehouse robotics market was valued at $9.88 billion in 2021, a Mordor Intelligence report states. It is expected to top $23.09 billion by 2027.
Smriti Jacob is Rochester Beacon managing editor.
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