Real time local wind inference for robust autonomous navigation Spencer Folk

Format:

Book

Thesis/Dissertation

Author/Creator:

Folk, Spencer, author.

Contributor:

University of Pennsylvania. Mechanical Engineering and Applied Mechanics., degree granting institution.

Language:

English

Subjects (All):

Robotics.

Engineering.

Urban planning.

0771.

0800.

0537.

0999.

Local Subjects:

Robotics.

Engineering.

Urban planning.

0771.

0800.

0537.

0999.

Genre:

Academic theses

Physical Description:

1 online resource (153 pages)

Contained In:

Dissertations Abstracts International 87-12A

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2026

Language Note:

English

Summary:

Urban air mobility and autonomous package delivery represent promising avenues for integrating aerial robots into everyday life. However, operating these systems safely and efficiently in windy urban environments remains a major challenge due to the complexity of urban wind flow fields. Existing methods for predicting and navigating wind fields rely on precise environmental knowledge, distributed sensor networks, or extensive exploration. On the contrary, 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

Notes:

Source: Dissertations Abstracts International, Volume: 87-12, Section: A.

Advisors: Kumar, Vijay; Yim, Mark Committee members: Hsieh, M. Ani; Kothmann, Bruce; Melton, John

Ph.D. University of Pennsylvania 2026

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798247972716

Access Restriction:

Restricted for use by site license