End-to-End Thermal Updraft Detection and Estimation for Autonomous Soaring Using Temporal Convolutional Networks

Abstract

Exploiting thermal updrafts to gain altitude can significantly extend the endurance of fixed-wing aircraft, as has been demonstrated by human glider pilots for decades. In this work, we present a novel end-to-end deep learning approach for the simultaneous detection of multiple thermal updrafts and the estimation of their properties — a key capability to let autonomous unmanned aerial vehicles soar as well. In contrast to previous works, our approach does not require separate algorithms for the detection of individual updrafts. Instead, a sequence of sensor measurements from a time window of interest can be directly fed into our temporal convolutional network, which estimates the position, strength, and spread of the encountered updrafts. We demonstrated in simulations that our approach can reliably detect updrafts solely based on measurements of the aircraft’s position and the local vertical wind velocity. Nevertheless, our method can additionally make use of measurements of the roll moment induced by updrafts, which improves the precision further. Compared with a particle-filter-based method, we can determine the correct number of encountered updrafts with an accuracy of 99.99% instead of 79.50%, significantly improve the precision of strength as well as spread estimates, and reduce the computational demand.