用蜜蜂视觉机制指导无人机避障，仅靠光流信息实现稳定飞行。
Understanding visual attention beehind bee-inspired UAV navigation
- 训练强化学习模型仅用光流感知环境导航
- 模型聚焦光流不连续和高幅度区域，避开障碍物并保持居中
- 跨模型一致的行为模式可为真实无人机提供简单控制策略
生物启发设计常用于自主无人机导航，因其在感官与计算能力有限的情况下仍能实现飞行与避障。蜜蜂主要依赖视网膜光流（optic flow）——视觉场中物体的表观运动——来穿越复杂环境。本文训练一个强化学习代理，在仅使用光流作为感知输入的条件下完成隧道障碍物导航任务。通过分析训练后代理的注意力模式，发现其主要关注光流不连续性区域及光流幅值较大的区域。这些代理通过避免产生大光流的障碍物，同时保持在环境中心位置进行导航，行为模式与真实昆虫相似。该注意力模式在多个独立训练的代理中均保持一致，表明其可能是一种适用于物理无人机的简单显式控制策略。
原文摘要 · Abstract (English)
Bio-inspired design is often used in autonomous UAV navigation due to the capacity of biological systems for flight and obstacle avoidance despite limited sensory and computational capabilities. In particular, honeybees mainly use the sensory input of optic flow, the apparent motion of objects in their visual field, to navigate cluttered environments. In our work, we train a Reinforcement Learning agent to navigate a tunnel with obstacles using only optic flow as sensory input. We inspect the attention patterns of trained agents to determine the regions of optic flow on which they primarily base their motor decisions. We find that agents trained in this way pay most attention to regions of discontinuity in optic flow, as well as regions with large optic flow magnitude. The trained agents appear to navigate a cluttered tunnel by avoiding the obstacles that produce large optic flow, while maintaining a centered position in their environment, which resembles the behavior seen in flying insects. This pattern persists across independently trained agents, which suggests that this could be a good strategy for developing a simple explicit control law for physical UAVs.
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