Real Time Local Wind Inference for Robust Autonomous Navigation

Abstract

This thesis presents a solution that enables aerial robots to reason about surrounding wind flow fields in real time using on board sensors and embedded flight hardware. The core novelty of this research is the fusion of range measurements with sparse in situ wind measurements to predict surrounding flow fields. We aim to address two fundamental questions: first, the sufficiency of topographical data for accurate wind prediction in dense urban environments; and second, the utility of learned wind models for motion planning with an emphasis on energy efficiency and obstacle avoidance. Drawing on tools from deep learning, fluid mechanics, and optimal control, we establish a framework for local wind prediction using navigational LiDAR, and then incorporate local wind model priors into a receding-horizon optimal controller to study how local wind knowledge affects energy use and robustness during autonomous navigation. Through simulated demonstrations in diverse urban wind scenarios we evaluate the predictive capabilities of the wind predictor, and quantify improvements to autonomous urban navigation in terms of crash rates and energy consumption when local wind information is integrated into the motion planning. Sub-scale free flight experiments in an open-air wind tunnel demonstrate that these algorithms can run in real time on an embedded flight computer with sufficient bandwidth for stable control of a small aerial robot. Philosophically, this thesis contributes a new paradigm for localized wind inference and motion planning in unknown windy environments. By enabling robots to rapidly assess local wind conditions without prior environmental knowledge, this research accelerates the introduction of aerial robots into increasingly challenging environments.

Figures (40)

• Figure 1: Free body diagram of a rotary-wing UAV subjected to forces and torques generated by gravity and aerodynamic interactions with the air. The blue, red, and black arrows indicate coordinate frames, wrenches, and positional vectors, respectively.
• Figure 2: Empirically-driven power curves for a rotary-wing UAV as a function of ground speed and wind speed. On the left, power curves are plotted for varying headwinds (-) and tailwinds (+) with the max range indicated for each. On the right, the optimal range airspeeds and ground speeds are plotted versus the wind speed.
• Figure 3: Monte Carlo evaluation of the Wind UKF over $50$ simulations; each instance has randomized mass, drag coefficients, and average wind magnitudes.
• Figure 4: A simulated instance of the unscented Kalman filter estimating the local wind velocity vector for a UAV subject to Dryden wind gusts.
• Figure 5: Method overview for predicting local wind vector fields in real time without a map of the environment. Buildings are sensed using range sensors and fed through a deep neural network alongside sparse wind measurements to predict the components of the time-averaged wind flow field.