FDA 集群：面向未来的集群行为
FDA Flocking: Future Direction-Aware Flocking via Velocity Prediction
AI总结：
本文提出FDA集群方法，通过预测邻居未来速度增强反应性集群的协调性和鲁棒性，实验表明其在对齐速度和抗干扰能力上优于传统反应性模型。
摘要：
理解自然界中的自组织集体，如鸟群，启发了群体机器人，但大多数集群模型仍然具有反应性，忽略了能够增强协调的前瞻性线索。受鸟类姿势和翼拍信号以及多旋翼姿态倾斜前方向变化的启发，本文引入了一种原理性、生物启发的前瞻性增强反应性集群行为，称为面向未来的（FDA）集群。在所提出的框架中，代理将反应性对齐与基于短期邻居未来速度估计的预测项结合，通过可调节的混合参数在反应性和前瞻性行为之间进行插值。这种预测结构增强了速度共识和凝聚力-分离平衡，同时缓解了传感和通信延迟以及测量噪声对反应性基线的不利影响。模拟结果表明，FDA 相比纯反应性模型实现了更快和更高的对齐，增强了集群的翻译位移，并在延迟和噪声方面表现出更好的鲁棒性。未来的工作将研究自适应混合策略、加权预测方案以及在多旋翼无人机集群上的实验验证。
英文摘要：
Understanding self-organization in natural collectives such as bird flocks inspires swarm robotics, yet most flocking models remain reactive, overlooking anticipatory cues that enhance coordination. Motivated by avian postural and wingbeat signals, as well as multirotor attitude tilts that precede directional changes, this work introduces a principled, bio-inspired anticipatory augmentation of reactive flocking termed Future Direction-Aware (FDA) flocking. In the proposed framework, agents blend reactive alignment with a predictive term based on short-term estimates of neighbors' future velocities, regulated by a tunable blending parameter that interpolates between reactive and anticipatory behaviors. This predictive structure enhances velocity consensus and cohesion-separation balance while mitigating the adverse effects of sensing and communication delays and measurement noise that destabilize reactive baselines. Simulation results demonstrate that FDA achieves faster and higher alignment, enhanced translational displacement of the flock, and improved robustness to delays and noise compared to a purely reactive model. Future work will investigate adaptive blending strategies, weighted prediction schemes, and experimental validation on multirotor drone swarms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
