# Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot Scenes

## TL;DR

This paper presents a neural-inspired vision system for micro-robots that effectively recognizes motion patterns like looming, recession, and translation, enabling timely collision avoidance and tracking in dynamic scenes.

## Contribution

A novel embedded neural network system combining insect-inspired neurons for motion pattern recognition in autonomous micro-robots.

## Key findings

- Effective recognition of basic motion patterns in robot scenes
- Improved collision detection in multi-robot arena tests
- Timely robot behaviors based on neural motion perception

## Abstract

Insects have tiny brains but complicated visual systems for motion perception. A handful of insect visual neurons have been computationally modeled and successfully applied for robotics. How different neurons collaborate on motion perception, is an open question to date. In this paper, we propose a novel embedded vision system in autonomous micro-robots, to recognize motion patterns in dynamic robot scenes. Here, the basic motion patterns are categorized into movements of looming (proximity), recession, translation, and other irrelevant ones. The presented system is a synthetic neural network, which comprises two complementary sub-systems with four spiking neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for sensing looming and recession, and the direction selective neurons (DSN-R and DSN-L) in flies for translational motion extraction. Images are transformed to spikes via spatiotemporal computations towards a switch function and decision making mechanisms, in order to invoke proper robot behaviors amongst collision avoidance, tracking and wandering, in dynamic robot scenes. Our robot experiments demonstrated two main contributions: (1) This neural vision system is effective to recognize the basic motion patterns corresponding to timely and proper robot behaviors in dynamic scenes. (2) The arena tests with multi-robots demonstrated the effectiveness in recognizing more abundant motion features for collision detection, which is a great improvement compared with former studies.