让无人机群提前预判邻居方向，提升编队稳定性与响应速度
FDA Flocking: Future Direction-Aware Flocking via Velocity Prediction
- 通过预测邻居短期速度实现前瞻式编队，融合反应与预见行为
- 相比纯反应模型，对齐速度更快，群体位移更远，抗延迟噪声更强
- 适合多旋翼无人机集群控制，尤其在通信延迟场景下表现优异
理解鸟群等自然集体的自组织行为启发了集群机器人研究，但现有编队模型多为被动响应型，忽略了增强协调性的前瞻信号。受鸟类姿态、拍翅信号及多旋翼飞行器姿态倾斜预示方向变化的启发，本文提出一种基于生物机理的前瞻性编队框架——未来方向感知（FDA） flocking。该框架中，智能体将反应性对齐与基于邻居未来速度短期预测的项相结合，由可调混合参数在反应与前瞻行为间插值。这种预测结构提升了速度一致性与群体聚散平衡，同时缓解了传感、通信延迟及测量噪声带来的不稳定性。仿真结果表明，与纯反应模型相比，FDA实现了更快更优的对齐、更大的群体平移位移，并展现出更强的抗延迟和噪声鲁棒性。未来工作将探索自适应混合策略、加权预测机制以及在多旋翼无人机集群上的实验验证。
原文摘要 · Abstract (English)
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.
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