Route-centric ant-inspired memories enable panoramic route-following in a car-like robot

Gabriel G. Gattaux, Antoine Wystrach (), Julien R. Serres and Franck Ruffier ()
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Gabriel G. Gattaux: CNRS, ISM
Antoine Wystrach: CRCA, CBI, UMR CNRS-UPS 5169
Julien R. Serres: CNRS, ISM
Franck Ruffier: CNRS, ISM

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Solitary foraging ants excel at route following using minimal neural resources, Robots don’t. Recent biological studies proposed lateralized, nest-centric memories to explain ants’ direct visual homing but did not address how ants follow curved visual routes away from their nest. We present a biologically inspired neuromorphic model for one-shot panoramic route learning and continuous route following, implemented on a compact car-like robot, Antcar. We demonstrate that route-centric lateralized memories, inspired by the insect mushroom body, enable Antcar to achieve bi-directional route-following, with motivation-driven recognition of route extremities and familiarity-based velocity control. With rigorous Lyapunov-based stability analysis and an empirical memory scalability evaluation, the model was tested over 1.6 km across 113 challenging real-world trials. The system achieves less than 25 cm median lateral error using minimal resources (800-pixel input, 300 MB RAM, 500 mW power, and 18.75 kB memory per 50 m route), offering insights into insect cognition and advancing autonomous robotics under strict resource constraints.

Date: 2025
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DOI: 10.1038/s41467-025-62327-3

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