Soaring strategies are redefining the flight capabilities of small-class fixed-wing unmanned aerial vehicles. This paper presents 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 estimates and mission priorities in real time for navigation through a wind field. This work addresses the question of ALFA’s ability to handle classified and unclassified updrafts. Instead of explicitly considering the classification of the updraft, the ALFA measures updraft data along an aircraft’s flight path, estimates updraft data ahead of the aircraft, generates candidate flight paths ahead of the aircraft for evaluation, and then selects the best candidate flight path based on a reward function. This paper describes the structure of ALFA and the tuning processes used for the updraft estimator and the reward function. Flight results demonstrate the ALFA’s ability to harvest atmospheric energy from classified and unclassified updrafts. The results discuss several produced flight behaviors in more detail, examining the ALFA’s effectiveness when flying among classified updrafts. Finally, this paper concludes that harvesting energy from the atmosphere with real-time local decision making is practically feasible and suggests that autonomous flight design and control strategies for small-class fixed-wing aircraft will likely be driven by harvesting energy from the atmosphere.