A sensorless wind-aware multimodal feedback framework for human-drone interaction in urban environments

Unmanned aerial vehicles (UAVs) are increasingly applied to urban delivery, infrastructure inspection, and emergency response, yet operation in dense built environments remains difficult because building-induced turbulence introduces invisible aerodynamic disturbances that impair control performance, increase cognitive workload, and weaken trust in autonomy. This study presents a sensorless wind-aware multimodal feedback framework for human-drone interaction in urban environments. The framework combines a Unity-based urban simulation environment, a sensorless wind estimation and wind-field reconstruction method, an immersive visual interface, torso-mounted haptic feedback, and an adaptive auditory warning channel. Instead of relying on a dedicated wind sensor, the proposed method derives a simulation-grounded estimate of local wind conditions from the drone’s dynamic response and uses this inferred wind representation to drive multimodal feedback for environmental awareness. A controlled experiment with 30 participants evaluated four feedback conditions: Control, Visual, Visual + Haptic, and Visual + Haptic + Audio. Results indicate that Visual + Haptic feedback provided the most effective trade-off among navigation efficiency, safety, workload, and trust, while the tri-modal condition further improved task efficiency at the cost of higher cognitive demand. These results suggest that integrating sensorless environmental estimation with multimodal interface design is a promising direction for improving human-centered UAV operation in complex urban environments.