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This study introduces a novel neuromorphic model inspired by ant navigation for robots. The Antcar robot demonstrates efficient, resource-minimal route following using biologically inspired memory systems.

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Area of Science:

  • Robotics
  • Neuroscience
  • Insect Cognition

Background:

  • Solitary foraging ants exhibit remarkable route-following capabilities using limited neural resources, a feat not yet replicated in robotics.
  • Existing models explain ant homing but not curved route following away from the nest.

Purpose of the Study:

  • To develop a biologically inspired neuromorphic model for one-shot panoramic route learning and continuous route following in robots.
  • To enable robots to navigate complex routes using minimal computational and memory resources, mimicking insect navigation.

Main Methods:

  • Implementation of a neuromorphic model on a compact car-like robot (Antcar).
  • Utilizing route-centric lateralized memories, inspired by the insect mushroom body, for bi-directional route following.
  • Incorporating motivation-driven recognition of route extremities and familiarity-based velocity control.

Main Results:

  • Antcar achieved robust bi-directional route-following in 113 real-world trials over 1.6 km.
  • The system demonstrated a median lateral error of less than 25 cm.
  • The model operates with minimal resources: 800-pixel input, 300 MB RAM, 500 mW power, and 18.75 kB memory per 50 m route.

Conclusions:

  • Biologically inspired lateralized memories enable efficient and resource-minimal route following in autonomous robots.
  • The model offers insights into insect cognition and advances autonomous robotics under strict resource constraints.
  • This approach paves the way for more capable robots in environments with limited power and memory.