A Sampling‐Based Path Planning Algorithm for Improving Observations in Tropical Cyclones

Earth and Space Science

Masashi Minamide¹  Justice Darko²  Larkin Folsom²  Hyoshin Park²  Masahiro Ono³  Hui Su³ 

[1] Department of Civil Engineering The University of Tokyo Bunkyo‐ku Japan;Department of Computational Science & Engineering North Carolina A&T State University Greensboro NC USA;Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA;

关键词: path planning;    unmanned aerial system;    observing system;    tropical cyclone;    targeted observation;   

DOI  :  10.1029/2020EA001498

来源: DOAJ

【 摘 要 】

Abstract Lack of high‐resolution observations in the inner‐core of tropical cyclones remains a key issue when constructing an accurate initial state of the storm structure. The major implication of an improper initial state is the poor predictability of the future state of the storm. The size and associated hazard from strong winds at the inner‐core make it impossible to sample this region entirely. However, targeting regions of the inner‐core where forecasted atmospheric measurements have high uncertainty can significantly improve the accuracy of measurements for the initial state of the storm. This study provides a scheme for targeted high‐resolution observations for small Unmanned Aircraft Systems (sUAS) platforms (e.g., Coyote sUAS) to improve the estimates of the atmospheric measurement in the inner‐core structure. The benefit of observation is calculated based on the high‐fidelity state‐of‐the‐art hurricane ensemble data assimilation system. Potential locations with the most informative measurements are identified through exploration of various simulation‐based solutions depending on the state variables (e.g., pressure, temperature, wind speed, relative humidity) and a combined representation of those variables. A sampling‐based sUAS path planning algorithm considers energy usage when locating the regions of highly uncertain prediction of measurements, allowing sUAS to maximize the benefit of observation. Robustness analysis of our algorithm for multiple scenarios of sUAS drop and goal locations shows satisfactory performance against benchmark similar to current NOAA field campaign. With optimized sUAS observations, a data assimilation analysis shows significant improvements of up to 4% in the tropical cyclone structure estimates after resolving uncertainties at targeted locations.

【 授权许可】

Unknown