Wu Haotian, Yue Shigang, Hu Cheng
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China.
Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou, China.
Front Neurorobot. 2024 Jan 25;18:1349498. doi: 10.3389/fnbot.2024.1349498. eCollection 2024.
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.
昆虫在复杂的自然环境中展现出非凡的导航能力，无论是躲避捕食者、捕捉猎物还是寻找同类，所有这些都依赖于它们紧凑而可靠的神经系统。我们探索受生物启发的机器人视觉系统领域，重点关注受蝗虫启发的小叶巨运动检测器（LGMD）模型。对现有的LGMD模型进行了全面评估，确定了其对功能至关重要的共同元特性。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，这对于增强受生物启发的模型在各种应用中的能力至关重要。该分析的结果是战略原型，它体现了所确定的元特性。它代表了一种开发更具响应性和适应性的机器人视觉系统的模块化且更灵活的方法。该观点强调了战略原型：LGMD通用原型（LGMD-UP）的潜力，它是重新构建LGMD模型以及推进我们对机器人中受生物启发的视觉系统的理解和实现的关键。它可能为研究和实际应用开辟更灵活、适应性更强的途径。
