ABSTRACT Reliable environmental perception for small autonomous unmanned aerial vehicles (UAVs) remains challenging under rapid ego‐motion, visual blind regions, and aerodynamic disturbances. Inspired by birds’ efficient sensing‐to‐computing pathways, we design a multimodal joint‐modulation hardware system in which a 2D floating‐gate (FG) memory serves as the computing core, integrating visual, inertial, and wind‐field cues to enable fast and stable tracking and obstacle avoidance in dynamic environments. We develop a MoS 2 /h‐BN/graphene FG device that provides stable multilevel conductance states, an on/off ratio above 10 8 , sub‐10 µs switching, long retention, and high device uniformity. A 4 × 4 FG‐memory array robustly encodes temporal visual variations for real‐time target tracking, while a single FG device acts as an airflow neuron that rapidly detects UAV‐induced airflow in visual blind regions. An inertial‐information‐driven adaptive threshold modulation scheme further stabilizes both pathways under rapid ego‐motion, enabling bird‐like tracking and avoidance. Experiments show that visual processing latency is ∼7 ms, the average tracking center offset rate is 11.5%, background drift suppression exceeds 80%, and airflow disturbances trigger avoidance within 2 ms. These results demonstrate that the proposed system significantly improves signal‐processing speed and robustness, enhancing UAV applicability in unstructured environments.
Guo et al. (Sun,) studied this question.