SYDNEY, June 19 (Xinhua) -- Australian researchers have unveiled a robot navigation system that mimics the neural processes of the human brain, dramatically reducing energy consumption.

The new system, called Locational Encoding with Neuromorphic Systems (LENS), uses brain-inspired computing and requires less than 10 percent of the energy needed by traditional robotic navigation technologies, according to a release from the Queensland University of Technology (QUT) on Thursday.

QUT robotics researchers leveraged neuromorphic computing, which processes information using electrical spikes similar to those in human neurons, to achieve real-time, energy-efficient location tracking.

"Energy constraints are a major challenge in real-world robotics, especially in fields like search and rescue, space exploration and underwater navigation," said the study's first author, neuroscientist Adam Hines.

Neuromorphic computing cuts the system's visual localization energy use by up to 99 percent, allowing robots to run longer and travel farther on limited power, Hines said.

"This system demonstrates how neuromorphic computing can achieve real-time, energy-efficient location tracking on robots, opening up new possibilities for low-power navigation technology," he said.

LENS integrates a spiking neural network, an event camera - mimicking human vision by detecting pixel-level brightness changes, and a neuromorphic chip, which enables location recognition over 8 km using just 180 KB of storage, 300 times less than conventional systems, said the study published in the journal Science Robotics.

Researchers highlighted that energy-efficient navigation is key to extending robot range and endurance, underscoring the team's focus on practical, user-oriented robotics innovation.