Robots get brain-like navigation to run for days using 90% less power

Researchers at the QUT Centre for Robotics have developed a brain-inspired robot navigation system called Locational Encoding with Neuromorphic Systems (LENS) that operates using less than 10% of the energy required by conventional navigation systems. By mimicking the human brain’s efficient processing, LENS uses specialized algorithms that process information as electrical spikes, similar to neuronal signals. This neuromorphic computing approach drastically reduces the energy consumption for visual localization by up to 99%, enabling robots to operate longer and travel further on limited power supplies. The system demonstrated effective location recognition along an 8 km route while requiring only 180KB of storage, which is about 300 times smaller than traditional systems. LENS achieves its efficiency through a combination of advanced technologies, including an event camera that detects pixel-level brightness changes continuously rather than capturing full images, closely replicating human visual processing. This “movement-focused” data is then processed by a spiking neural network on a low-power chip within a compact system. Such