<figure><img src="image_1.png" /></figure>

HAL
open science

# AntCar, Parsimonious Navigation: Ants-Inspired Visual Learning in a Car-like Robot

Gabriel G Gattaux, Antoine Wystrach, Julien Serres, Franck Ruffier

► To cite this version:

Gabriel G Gattaux, Antoine Wystrach, Julien Serres, Franck Ruffier. AntCar, Parsimonious Navigation: Ants-Inspired Visual Learning in a Car-like Robot. 19ème Journée de l'École Doctorale Sciences du Mouvement Humain ED463 (JEDSMH 2024), May 2024, Marseille, France., 2024. [hal-05357376](https://hal.science/hal-05357376v1)

HAL Id: hal-05357376

https://hal.science/hal-05357376v1

Submitted on 10 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# AntCar, Parsimonious Navigation : Ants-Inspired Visual Learning in a Car-like Robot

(Aix*Marseille
université
Socialement engagée)

CRCA

<figure><img src="image_3.png" /></figure>

Gabriel Gattaux¹, Antoine Wystrach², Julien Serres¹, Franck Ruffier¹

¹ Aix Marseille Univ., CNRS, ISM, SBI, Marseille, France
² Toulouse III Univ., CNRS, CRCA, CBI, Toulouse, France

<figure><img src="image_4.png" /></figure>

AGENCE
INNOVATION
DÉFENSE

<figure><img src="image_5.png" /></figure>

DGA

<figure><img src="image_6.png" /></figure>

MINISTÈRE
DES ARMÉES

**Abstract** : This research addresses the challenge of autonomous navigation in robotics, highlighted by the resource-intensive demands of mapping techniques such as SLAM with LiDAR or stereoscopic vision. Inspired by the efficiency observed in nature, in particular insect navigation, this study strives to reproduce the neural mechanisms and behaviors of species such as Cataglyphys velox in the context of a specific task. The study focuses on the neural circuits of mushroom bodies, the epicenter of long-term memory in insects, facilitating visual navigation and assessment of scene familiarity. The study presents the first integration of an ant-inspired parsimonious neural network on a low-cost robot, Antcar, demonstrating one-shot online learning, achieving autonomous control of speed and direction by oscillating between two locations connected by a road.

**Keywords** : Visual learning - Ants-inspired robotic - Mushroom Bodies - Laterlized Memories - Route Following

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

## References

[1] - M. Mangan and B. Webb, 2012, Spontaneous formation of multiple routes in individual desert ants
[2] - Zeil, J. Visual navigation: properties, acquisition and use of views. J Comp Physiol A (2022)
[3] - Ardin, P., Peng, F., Mangan, M., Lagogiannis, K. & Webb, B. Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments. PLoS Comput Biol 12, e1004683 (2016).
[4] - Le, Z. Neuromorphic sequence learning with an event camera on routes through vegetation. (2023) doi:10.5281/ZENODO.8289547.
[5] - Wystrach, A., Le Moël, F., Clement, L. & Schwarz, S. A lateralised design for the interaction of visual memories and heading representations in navigating ants. (2020) doi:10.1101/2020.08.13.249193.
Gattaux, G. Wystrach, A. Serres, J. Ruffier, F. Antcar: Simple Route following task with ants-inspired vision and neural model, (2023) Hal

## Funding

Thanks to Clement Serasse and Thomas Gaillard for their help during the experiments. This research was supported by the Agence de l'Innovation de Défense (AID), CNRS, Aix Marseille University, the Provence-Alpes-Côte d'Azurregion. This work has been partially supported by ROBOTEX2.0 (Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREXANR-21-ESRE-0015).