Soaring strategies are redefining the flight capabilities of small-class fixed-wing unmanned aerial vehicles.This paperpresents an autonomous soaring strategy that exploits updraft energy independent of the classification of an updraft.The strategy employs an artificial lumbered flight algorithm (ALFA) that weighs near-field updraft velocity estimatesand mission priorities in real time for navigation through a wind field. This work addresses the question of ALFA’sability to handle classified and unclassified updrafts. Instead of explicitly considering the classification of the updraft,the ALFAmeasures updraft data along an aircraft’s flight path, estimates updraft data ahead of the aircraft, generatescandidate flight paths ahead of the aircraft for evaluation, and then selects the best candidate flight path based on areward function. This paper describes the structure of ALFA and the tuning processes used for the updraft estimatorand the reward function. Flight results demonstrate the ALFA’s ability to harvest atmospheric energy from classifiedand unclassified updrafts. The results discuss several produced flight behaviors in more detail, examining the ALFA’seffectiveness when flying among classified updrafts. Finally, this paper concludes that harvesting energy from theatmosphere with real-time local decisionmaking is practically feasible and suggests that autonomous flight design andcontrol strategies for small-class fixed-wing aircraft will likely be driven by harvesting energy from the atmosphere.
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