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.

翻译：理解鸟群等自然群体中的自组织现象为集群机器人学提供了灵感，然而大多数集群模型仍停留在反应式层面，忽视了能够增强协调性的预见性线索。受鸟类姿态与翼拍信号以及多旋翼飞行器在方向改变前姿态倾斜现象的启发，本研究提出了一种原理性的、受生物启发的反应式集群预见性增强方法，称为未来方向感知集群。在所提出的框架中，智能体将反应式对齐与基于邻居未来速度短期估计的预测项相结合，并通过一个可调混合参数进行调节，该参数在反应式与预见性行为之间进行插值。这种预测结构增强了速度一致性与内聚-分离平衡，同时减轻了传感与通信延迟以及测量噪声的不利影响，这些因素会破坏反应式基线的稳定性。仿真结果表明，与纯反应式模型相比，FDA实现了更快、更高程度的一致性、增强的集群整体平移位移，以及对延迟和噪声的更强鲁棒性。未来工作将研究自适应混合策略、加权预测方案，并在多旋翼无人机集群上进行实验验证。