Autonomous Thermalling as a Partially Observable Markov Decision Process (Extended Version)
This addresses flight endurance for small UAVs by enabling better use of atmospheric updrafts, though it is incremental as it builds on existing POMDP methods.
The paper tackled the problem of limited flight time for small UAVs by modeling autonomous thermalling as a POMDP and implementing a receding-horizon controller, which showed a significant advantage over an existing alternative in live flight tests.
Small uninhabited aerial vehicles (sUAVs) commonly rely on active propulsion to stay airborne, which limits flight time and range. To address this, autonomous soaring seeks to utilize free atmospheric energy in the form of updrafts (thermals). However, their irregular nature at low altitudes makes them hard to exploit for existing methods. We model autonomous thermalling as a POMDP and present a receding-horizon controller based on it. We implement it as part of ArduPlane, a popular open-source autopilot, and compare it to an existing alternative in a series of live flight tests involving two sUAVs thermalling simultaneously, with our POMDP-based controller showing a significant advantage.