基于视觉引导的群体运动协调：受昆虫启发的小目标运动反应
Visually Guided Swarm Motion Coordination via Insect-inspired Small Target Motion Reactions
AI总结：
本研究提出受昆虫STMD神经元启发的小目标运动反应（STMR）群体方法，通过峰值光流方位角设计反馈切换控制系统，实现低连接性下的群体运动协调并验证其稳定性。
摘要：
尽管在开发与实验一致的昆虫飞行感知与反馈模型方面取得了进展，但缺乏对由此产生的仿生感知与反馈方法在多智能体和群体性能上的系统理解，仍是机器人群体实现的障碍。本研究通过设计在昆虫小叶复合体中发现的小目标运动检测器（STMD）神经元的简明工程模型，提出了小目标运动反应（STMR）群体方法。STMD神经元模型识别峰值光流幅值出现的方位角，并利用该角度设计输出反馈切换控制系统。理论稳定性分析提供了群体环境下的双智能体稳定性和状态有界性。该方法在地面车辆上进行了仿真和实现，以进行验证和行为研究。结果表明，尽管其连接性低于当代方法（每个智能体瞬时仅关注单个邻居），仍可实现群体集体运动。STMR群体级指标分析还突出了持续变化的极化和递减的航向方差。
英文摘要：
Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), collective group motion can be achieved. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.
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