Re-framing bio-plausible collision detection: identifying shared meta-properties through strategic prototyping

TL;DR

This paper explores how insect-inspired neural systems can improve robotic vision by identifying shared properties and proposing a flexible framework for better adaptability.

Contribution

The paper introduces the Strategic Prototype, a modular framework capturing essential meta-properties of bio-inspired models for robotic vision.

Findings

Common meta-properties in LGMD models include layered structures and computational strategies.

The Strategic Prototype offers a flexible method for developing responsive robotic visual systems.

The LGMD-UP framework could advance bio-inspired visual systems in robotics.

Abstract

Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications.

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