Visual Homing in Outdoor Robots Using Mushroom Body Circuits and Learning Walks

TL;DR

This paper introduces a biologically inspired visual homing system for robots using Mushroom Body circuits, demonstrating robust outdoor navigation with minimal sensory input and low computational resources, inspired by ant behavior.

Contribution

It is the first real-world implementation of lateralized Mushroom Body architecture for visual homing on a compact robot, integrating learning walks and path integration signals.

Findings

Successful real-world homing after learning walks

Robust homing in outdoor environments using minimal sensory data

Efficient system operating at 8 Hz on Raspberry Pi 4

Abstract

Ants achieve robust visual homing with minimal sensory input and only a few learning walks, inspiring biomimetic solutions for autonomous navigation. While Mushroom Body (MB) models have been used in robotic route following, they have not yet been applied to visual homing. We present the first real-world implementation of a lateralized MB architecture for visual homing onboard a compact autonomous car-like robot. We test whether the sign of the angular path integration (PI) signal can categorize panoramic views, acquired during learning walks and encoded in the MB, into "goal on the left" and "goal on the right" memory banks, enabling robust homing in natural outdoor settings. We validate this approach through four incremental experiments: (1) simulation showing attractor-like nest dynamics; (2) real-world homing after decoupled learning walks, producing nest search behavior; (3) homing after random walks using noisy PI emulated with GPS-RTK; and (4) precise stopping-at-the-goal behavior enabled by a fifth MB Output Neuron (MBON) encoding goal-views to control velocity. This mimics the accurate homing behavior of ants and functionally resembles waypoint-based position control in robotics, despite relying solely on visual input. Operating at 8 Hz on a Raspberry Pi 4 with 32x32 pixel views and a memory footprint under 9 kB, our system offers a biologically grounded, resource-efficient solution for autonomous visual homing.