Dec. 15 (UPI) -- To help robots walk a little bit more like real insects, researchers have turned to nonlinear physics, the branch of physics used to describe complex, real world systems.
"Nonlinear systems, such as the weather or neurons, often appear chaotic, unpredictable or counterintuitive, and yet their behavior is not random," according to the research journal Nature.
In a new study -- published Tuesday in the journal Chaos -- researchers in Japan and Italy used a Rössler system, a system of three nonlinear differential equations, to built a central pattern generator for their six-legged robot.
"The universal nature of underlying phenomena allowed us to demonstrate that locomotion can be achieved via elementary combinations of Rössler systems, which represent a cornerstone in the history of chaotic systems," study co-author Ludovico Minati said in a news release.
"These networks, CPGs, are the basis of legged locomotion everywhere within nature," said Minati, a researcher with the Tokyo Institute of Technology and the University of Trento.
Through synchronization of a trio of fairly simple nonlinear physical equations, Minati and his research partners were able to generate complex rhythmic patterns.
To begin, researchers linked the movement of the each of the six legs of their ant-like robot with a minimalistic network. To lend their system great chaos and complexity, researchers made slight adjustments to the network-leg coupling, yielding minor synchronization delays.
Because the changes in the outputs of nonlinear systems aren't proportional to input changes, these slight adjustments resulted in a dramatic increase in the complexity of the robot's locomotion pattern.
Researchers outfitted their central pattern generator with an electroencephalogram, or EEG device, which turned the CPG into a brain-computer interface.
"Neuroelectrical activity from a person is recorded and nonlinear concepts of phase synchronization are used to extract a pattern," said Minati. "This pattern is then used as a basis to influence the dynamics of the Rössler systems, which generate the walking pattern for the insect robot."
Neural patterns offer another layer of nonlinear dynamics, enhancing the system's complexity.
"First, we use them to decode biological activity, then in the opposite direction to generate bioinspired activity," Minati said.
Nonlinear systems like the Rössler system are often considered in only abstract terms, according to the study's authors, but the new research offers proof that the physics of chaos can help researchers "generate biologically plausible patterns."