Probabilistic Weather Forecasting Analysis for Unmanned Aerial Vehicle Path Planning

摘要

This Note shows that a probabilistic weather map can be constructed from ensemble forecasting with confidence bounds. With this map, a three-dimensional field D* searching algorithm is used to find the optimal fly route in the sense of the defined cost function. Through a series of probabilistic analysis, the probability of adverse weather in each cell of the weather map, the probability of mission failure, and their associated confidence bounds can be calculated to maximize the performance of the unmanned aerial vehicle. Simulation results based on real-time thunderstorm forecasting data are used to demonstrate the efficiency of the proposed approach. In future work, this approach will be extended to a path-planning problem with multiple types of adverse weather.