Enhancing Ant-Inspired Visual Compass with Focused Visual Scan in a Compact Robot

As the demand for autonomous robots grows, from self-driving cars to factory automation, so does the need for resource-efficient navigation algorithms. This challenge has inspired the development of sparse neural networks for visual navigation, drawing particularly from bio-inspired, ant-based neuromorphic artificial intelligence algorithms. Recent approaches leverage the neural architecture of the mushroom body in ants to learn and follow routes by recognizing familiar visual patterns. However, such models have not yet been implemented in closed-loop, resource-constrained robotic systems with satisfying performance. In this work, we present a mushroom body-inspired model embedded in the Antcar, a compact, car-like robot that processes panoramic images at low resolution. Our experiments reveal that reducing the scanning range and resolution improves visual compass performance for robot’s route-following. These findings highlight the trade-off between increased refresh rates and the benefits of selective perception, paving the way for more efficient real-time decision-making in resource-constrained systems.