WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors
WAND is a reinforcement learning framework that helps quadrotors navigate cluttered environments despite strong wind disturbances. It uses a Temporal Convolutional Network (TCN) to estimate wind-induced disturbance acceleration from past flight data. This estimate is used in two ways: conditioning the navigation policy and providing feedforward compensation for low-level control, significantly improving robustness against wind.