Low-energy robot navigation mimics neural processes
Robotics researchers have developed LENS, a new robot navigation system that mimics neural processes and uses under 10 per cent of the energy required by traditional systems.
Developed at Queensland University of Technology (QUT), LENS (Locational Encoding with Neuromorphic Systems) uses brain-inspired computing to set a new, low-energy benchmark for robotic place recognition. The breakthrough is detailed in Science Robotics.
The research, conducted by first author neuroscientist Dr Adam Hines along with Professor Michael Milford and Dr Tobias Fischer, all from the QUT Centre of Robotics and the QUT School of Electrical Engineering and Robotics, uses neuromorphic computing
“To run these neuromorphic systems, we designed specialised algorithms that learn more like humans do, processing information in the form of electrical spikes, similar to the signals used by real neurons,” Dr Hines said in a statement.
“Energy constraints are a major challenge in real-world robotics, especially in fields like search and rescue, space exploration and underwater navigation.
“By using neuromorphic computing, our system reduces the energy requirements of visual localisation by up to 99 per cent, allowing robots to operate longer and cover greater distances on limited power supplies.
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