Artificial Lumbered Flight for Autonomous Soaring

Abstract

POWERS, THOMAS CORNELIUS. Artificial Lumbered Flight for Autonomous Soaring. (Under the direction of Drs. Larry Silverberg and Ashok Gopalarathnam).Soaring strategies are redefining the flight capabilities of small-class fixed-wing UAVs. This dissertation 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 for navigation. This work raises the question of ALFA's ability to handle classified updrafts. Indeed, ALFA does not explicitly consider the classification of the updraft. Instead, ALFA measures updraft data along an aircraft's flight path, estimates updraft data ahead of the aircraft, generates candidate flightpaths ahead of the aircraft for evaluation, and then selects the best candidate flightpath based on a reward function. This dissertation describes the structure of ALFA and the tuning processes for the updraft estimator and the decision function.Flight results demonstrate the ability of artificial lumbered flight to harness atmospheric energy and complete its objectives. The flight results consider aircraft behavior in more detail, examining ALFA's effectiveness when flying among classified updrafts. They demonstrate the ability of artificial lumbered flight to navigate unclassified updrafts and harvest energy from thermal updrafts. Finally, this work highlights that autonomous flight design and control of small-class aircraft is maturing into its own flight regime that lies between the flapping flight and cruise flight regimes, and will be driven by the harvesting of energy from the atmosphere.

Energy-Harvesting Strategy Investigation for Glider Autonomous Soaring Using Reinforcement Learning

“…In this study, the glider is considered a rigid body model, and the impact of excessive maneuvering was ignored. Many simulation studies on autonomous soaring have set a large range of flight parameters for glider models [9,15,18]. Referring to these, we impose the following bounds on state values:…”

“…In further research, field flight experiments were successfully implemented, and an autonomous flight strategy directly applicable to turbulence environments was proposed. Thomas et al [9] used a new intelligent algorithm, the artificial lumbered flight algorithm, to conduct autonomous flight research for a powered UAV with a wingspan of 2 m. By setting the reward function and using intelligent algorithms to train UAVs in simulated wind fields, the researchers made an autonomous intelligence trade-off between navigating to a target point and using updraft to gain energy. Using fully trained intelligent algorithms as flight strategies, the researchers successfully implemented flight experiments, demonstrating the feasibility of the autonomous hovering of UAVs.…”

“…Autonomous soaring logically divides into a structured approach and "a lumbered approach" [9]. The structured approach means that the aircraft identifies updrafts by comparing updraft velocity measurements with knowledge of classified updrafts.…”

Opportunistic soaring by birds suggests new opportunities for atmospheric energy harvesting by flying robots

“…Interface 19: 20220671 preprogrammed waypoints: having detected a thermal directly, it is possible to fly an efficient trajectory using an explicit thermal-centring algorithm that makes use of inertial sensing of roll motion and acceleration as described above [6][7][8][9][10][11][12][140][141][142]. Other attempts have involved algorithms that plan a flight path by weighing near-field updraft velocity estimates [143], or dynamically mapping thermal updrafts [16,17]. Black-box algorithms acquired through reinforcement learning have also been reported [62,97].…”