PDF(Pubmed)
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.
摘要：
昆虫在复杂的自然环境中表现出非凡的能力，不管是逃避捕食者,捕获猎物,或者寻找细节，所有这些都依赖于它们紧凑而可靠的神经系统。我们探索生物启发的机器人视觉系统领域，专注于蝗虫启发的Lobula巨型运动探测器（LGMD）模型。对现有的LGMD模型进行了全面评估，确定它们的共同元属性，这些属性对它们的功能至关重要。这篇文章揭示了一个共同的框架，以分层结构和计算策略为特征，这对于增强生物启发模型在各种应用中的能力至关重要。分析的结果是战略原型，体现了已识别的元属性。它代表了一种模块化和更灵活的方法，用于开发更具响应性和适应性的机器人视觉系统。该观点突出了战略原型的潜力：LGMD-通用原型（LGMD-UP），重新构建LGMD模型并促进我们对机器人技术中生物启发视觉系统的理解和实施的关键。它可能会为研究和实际应用开辟更加灵活和适应性强的途径。
