A biological phenomenon the explains how termites build nests or how beavers build dams has led to a new class of robots that are able to overcome environmental obstacles to reach their target destination.

Scientists from the University of Buffalo have developed an autonomous robot built to tackle uneven terrain and other obstacles using a new set of algorithms, which could be particularly useful in search and rescue operations, planetary exploration for Mars rover-style vehicles and other areas.

“When a beaver builds a dam, it’s not following a blueprint,” Nils Napp, PhD, an assistant professor of computer science and engineering in Buffalo’s School of Engineering and Applied Sciences, said in a statement. “Instead, it’s reacting to moving water.

“It’s trying to stop the water from flowing,” he added. “We’re developing a system for autonomous robots to behave similarly. “The robot continuously monitors and modifies its terrain to make it more mobile.”

The researchers tapped into stigmergy, a biological phenomenon that helps explain a range of subjects including the behavior of termites and beavers.  

In stigmergy, the complex nests that termites build are not the result of well-defined plans or deep communication, but rather a type of indirect coordination.

Initially, a termite will deposit a pheromone-laced ball of mud in a random spot, and other termites attracted to the pheromones are likely to drop their mudballs at the same spot, leading to large termite nests.

Inspired by that concept, the researchers outfitted a mini-rover vehicle with a camera, custom software and a robotic arm that can lift and deposit objects. The team then build uneven terrain that included randomly place rocks, bricks and broken bits of concrete that simulate an environment at a disaster like a tornado or earthquake. They also placed hand-sized beanbags of various sizes in the simulated disaster area.

The researchers then sent out the robot using the new algorithms that enables it to continuously monitor and scan its environment. The robot picked up the beanbags and deposited them into holes and gaps in between the rock, brick and concrete.

Eventually the bags formed a ramp to allow the robot to overcome some of the obstacles standing in its way before it is able to reach its target location, a flat platform.

After 10 tests, the robot moved anywhere from 33 to 170 bags, each time creating a ramp which allowed it reach its target location.

“In this case, it’s like a beaver using nearby materials to build with,” Napp said. “The robot takes its cues from its surroundings, and it will keep modifying its environment until it has created a ramp. That means it can fix mistakes and react to disturbances; for example pesky researchers messing up half-built ramps, just like beavers that fix leaks in their dams.”