  Accurately estimating risk in real-time is essential for ensuring the safety
and efficiency of many applications involving autonomous robot systems. This
paper presents a novel, generalizable algorithm for the real-time estimation of
risks created by external disturbances on multirotors. Unlike conventional
approaches, our method requires no additional sensors, accurate drone models,
or large datasets. It employs motor command data in a fuzzy logic system,
overcoming barriers to real-world implementation. Inherently adaptable, it
utilizes fundamental drone characteristics, making it applicable to diverse
drone models. The efficiency of the algorithm has been confirmed through
comprehensive real-world testing on various platforms. It proficiently
discerned between high and low-risk scenarios resulting from diverse wind
disturbances and varying thrust-to-weight ratios. The algorithm surpassed the
widely-recognized ArduCopter wind estimation algorithm in performance and
demonstrated its capability to promptly detect brief gusts.
