How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots

2025-09-17

AI Summary

Micro-robots operating in complex, unknown environments face a fundamental trade-off between limited onboard computational resources and the need for agile, real-time collision avoidance. Method: Inspired by the fruit fly’s LPLC2 visual neurons, we propose a biologically grounded, ultra-lightweight spiking neural model (70 KB) embedded directly onto the Colias micro-robot. Our approach integrates a multi-attention mechanism emulating LPLC2’s distributed directional selectivity with embedded vision processing and neuromorphic motor control to generate low-overhead, adaptive escape behaviors. Contribution/Results: The resulting attention-driven perception–action coupling system achieves a 96.1% collision detection success rate and generates more flexible, graceful evasive maneuvers than prior locust-inspired models. This work demonstrates, for the first time, the feasibility and superiority of bio-mechanism-guided, resource-efficient neuromorphic control on severely constrained robotic platforms.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Bio-inspired Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Developing fly-inspired neural perception for robot agility
• Addressing computational cost vs performance trade-off
• Implementing LPLC2 neural model in micro robot vision

Innovation

• Fly-inspired neural model for collision perception
• Attention-driven visuomotor control strategy implementation
• Optimized 70KB embedded vision system