# Ant-inspired bilateral visual memories to guide a car-like robot

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

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Gabriel G Gattaux, Antoine Wystrach, Julien Serres, Franck Ruffier. Ant-inspired bilateral visual memories to guide a car-like robot. Journée des Jeunes Chercheurs en Robotique (JJCR 2023), GDR Robotique; Franck Ruffier; Maan El Badaoui El Najjar, Oct 2023, Moliets et Maâ, France. hal-05354130

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Submitted on 7 Nov 2025

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# Ant-inspired bilateral visual memories to guide a car-like robot

Gabriel Gattaux<fnref n="1" />, Antoine Wystrach<fnref n="2" />, Julien R. Serres<fnref n="1" />,<fnref n="3" />, Franck Ruffier<fnref n="1" />

**Keywords:** Route-following, Neurorobotic, Visual navigation, UGV.

Ants, like *Catagliphys velox* or *Melophorus bagotis*, which are recognized for their visual-based navigation skills are capable, with a single learning trial, to memorize long routes between points of interest, such as a feeder and their nest [2]. The current assumption in ant navigation, and only implemented in simulation, is a route-following behavior based on a visual compass, which involves determining the direction (by scanning), then maximizing a familiarity index with respect to views learnt along the path [1]. These views are learned in visual memory centers, called the Mushroom Bodies (MBs) [3] and then compared to compute a familiarity index from 0 to 1. In this study, our approach consists of interpreting a biologically plausible scan during learning and not anymore during exploitation [4], which strongly increases the algorithm efficiency for a homing task in robotics. Our vision-based algorithm could be useful during a GPS, SLAM or IMU failure.

<figure><img src="image_2.png" /><figcaption>Figure 1: Experimental robotics environment (Flight Mediterranean Arena) fitted with visual cues. A) Antcar, the car-like robot with ant's inspired processes. B) Extreme low resolution 32x32 pixels image learned. C) Showed a visually learned from Base to Goal and autonomously recovered L-shape backward route from Goal to Base. The accurate ground truth information is obtained through the VICON™ motion capture system covering 8 x 6 x 6m.</figcaption></figure>

<footnote n="1">¹Aix Marseille Univ, CNRS, ISM, Marseille, France</footnote>

<footnote n="2">²CRCA, CBI, CNRS, Toulouse III Univ., Toulouse, France</footnote>

<footnote n="3">³Institut Universitaire de France (IUF), Paris, France</footnote>
The Antcar robot (Fig. 1A), equipped with a 220-degree fisheye camera pointing skyward at extremely low resolution (Fig. 1B) of 32x32 pixels (representing 7 degrees per pixel). Following a one-shot learning path of 10m from Base to Goal, Antcar was successfully returned to the Base (Fig. 1C) on its 19 trials with an average path deviation of 19cm. The algorithm enabled a control refresh rate of 28Hz out of a maximum of 30Hz dictated by the frame rate of the embedded camera. In addition, the robot stops when it visually recognizes its base. The entire process consumed just 324KB of local memory and was run on a Raspberry Pi with 4GB of RAM.

* **Visual Route Recognition:** The technique for visually learning a road was inspired by the concept of memory lateralization observed in ants' neural networks and oscillations observed in their behaviors. *In silico*, Antcar rotated its image in the Frenet-Serret frame and learned it in a “left memory” or “right memory” with respect to the rotation direction. During the exploitation phase, the agent subtracted both memories, allowing it to determine precisely whether it was oriented to the left or right of the learned road without having to rotate or to scan the panorama.

* **Visual Place Recognition:** While place learning was not our primary contribution, we implemented a third memory where the agent looked in the direction of the base to visually learn its spatial layout. Subsequently, when passing through this base, the agent recognized the place through a "familiarity burst."

* **Steering control:** Simplifying the robot's Ackermann kinematics reduced the noise between the command and the real states of the robot, eliminating the need to estimate unnecessary states for the specific task. It was assumed that the steering command depended solely on the familiarity outputs, not on world frame orientations.

* **Speed control:** Drawing inspiration from the speed of ants under various conditions, familiarity was employed to modulate the robot's speed. The speed was reduced when the environment became unfamiliar and increased on the learned path.

**Acknowledgement:** We thank C.Serasse for his 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'Azur region. This work has been partially supported by ROBOTEX 2.0 (Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015).

## References

[1] Paul Ardin et al. "Using an insect mushroom body circuit to encode route memory in complex natural environments". In: *PLoS computational biology* 12.2 (2016), e1004683.

[2] Michael Mangan and Barbara Webb. "Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)". In: *Behavioral Ecology* 23.5 (2012), pp. 944-954.

[3] Barbara Webb and Antoine Wystrach. "Neural mechanisms of insect navigation". In: *Current Opinion in Insect Science* 15 (2016), pp. 27-39.

[4] Antoine Wystrach. "Neurons from pre-motor areas to the mushroom bodies can orchestrate latent visual learning in navigating insects." In: *bioRxiv* (2023), pp. 2023-03.