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.

中文翻译：

---

重新构建生物合理的碰撞检测：通过战略原型设计识别共享元属性

昆虫在复杂的自然环境中表现出非凡的能力，无论是躲避捕食者、捕获猎物还是寻找同类，所有这些都依赖于它们紧凑而可靠的神经系统。我们探索仿生机器人视觉系统领域，重点关注受蝗虫启发的小叶巨型运动探测器（LGMD）模型。现有的 LGMD 模型经过彻底评估，确定了对其功能至关重要的常见元属性。本文揭示了一个以分层结构和计算策略为特征的通用框架，这对于增强仿生模型针对不同应用的能力至关重要。该分析的结果是战略原型，它体现了已识别的元属性。它代表了一种模块化且更灵活的方法，用于开发响应更快、适应性更强的机器人视觉系统。该观点强调了战略原型的潜力：LGMD-通用原型（LGMD-UP），这是重新构建 LGMD 模型并促进我们对机器人仿生视觉系统的理解和实施的关键。它可能为研究和实际应用开辟更灵活、适应性更强的途径。