How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots

• URL: http://arxiv.org/abs/2509.13827v1
• Date: Wed, 17 Sep 2025 08:53:53 GMT
• Title: How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
• Authors: Renyuan Liu, Haoting Zhou, Chuankai Fang, Qinbing Fu,
• Abstract summary: We propose an attention-driven visuomotor control strategy inspired by a specific class of fly visual projection neurons.<n>This represents the first embodiment of an LPLC2 neural model in the embedded vision of a physical mobile robot.<n>Results showed that the fly-inspired visuomotor model achieved comparable robustness, at success rate of 96.1% in collision detection.
• Score: 1.1619569706231647
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Anyone who has tried to swat a fly has likely been frustrated by its remarkable agility.This ability stems from its visual neural perception system, particularly the collision-selective neurons within its small brain.For autonomous robots operating in complex and unfamiliar environments, achieving similar agility is highly desirable but often constrained by the trade-off between computational cost and performance.In this context, insect-inspired intelligence offers a parsimonious route to low-power, computationally efficient frameworks.In this paper, we propose an attention-driven visuomotor control strategy inspired by a specific class of fly visual projection neurons-the lobula plate/lobula column type-2 (LPLC2)-and their associated escape behaviors.To our knowledge, this represents the first embodiment of an LPLC2 neural model in the embedded vision of a physical mobile robot, enabling collision perception and reactive evasion.The model was simplified and optimized at 70KB in memory to suit the computational constraints of a vision-based micro robot, the Colias, while preserving key neural perception mechanisms.We further incorporated multi-attention mechanisms to emulate the distributed nature of LPLC2 responses, allowing the robot to detect and react to approaching targets both rapidly and selectively.We systematically evaluated the proposed method against a state-of-the-art locust-inspired collision detection model.Results showed that the fly-inspired visuomotor model achieved comparable robustness, at success rate of 96.1% in collision detection while producing more adaptive and elegant evasive maneuvers.Beyond demonstrating an effective collision-avoidance strategy, this work highlights the potential of fly-inspired neural models for advancing research into collective behaviors in insect intelligence.

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