Salt River Project looks to Arizona State robots to maintain canals
PHOENIX — The Salt River Project is turning to robots and drones to maintain miles of irrigation canals linked by even more miles of ditches and pipes.
The new method will make the operation work a lot more efficiently, as opposed to using resources to dispatch individuals, their trucks and equipment, to a remote location, said Mike Ploughe, a senior scientist with the Salt River Project (SRP) water quality and waste management services.
Beyond handling dirt and debris, SRP uses herbicides to reduce algae and sago pondweed in its 131 miles (211 kilometers) of open irrigation canals, which are connected by more than 1,000 miles of lateral ditches and pipes.
But those narrow lateral pipes, often choked with invasive freshwater mussels, pose a tougher challenge, KJZZ-FM reported .
Assistant professor Wenlong Zhang and his team from Arizona State University’s Robotics and Intelligent Systems Laboratory are working on a quadcopter that will fly itself to assigned locations, observe and await orders from operators.
The unmanned aerial vehicle may one day autonomously inspect remote areas and collect samples using an onboard soft grasper.
The lightweight flexible grasper is designed by Zhang’s research partner, professor and soft robotics expert Panagiotis Polygerinos, who leads ASU’s Bio-Inspired Mechatronics Lab.
In place of costly, heavy and rigid motors or gears, the grasper’s “fingers” rely on their segmented shape — and stiff or flexible areas — to curl when inflated.
Once an SRP engineer — or, eventually, an onboard artificial intelligence — selects an object for sampling or retrieval, Zhang’s drone will use advanced object recognition and self-piloting abilities to hover itself down to the target and get to work.
“Now the soft graspers, they will just go over the object, they will just start closing and conforming their fingers around whatever object that might be,” Polygerinos said.
The robots require solving some of the most intricate problems in robotics — including object recognition, autonomy, navigation, flight, machine learning — for a variety of environs and missions, using minimal energy and limited computing power.
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