Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation
Name
Willcox_Methodology for path.pdf
Size
3.8 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Singh, Victor
Willcox, Karen E
Date Issued
June 2016
Journal
17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
Publisher
American Institute of Aeronautics and Astronautics
Citation
Singh, Victor, and Karen E. Willcox. “Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation.” American Institute of Aeronautics and Astronautics, 2016.
Version
Author's final manuscript
Abstract
This paper presents methodology to enable path planning for an unmanned aerial vehicle that uses dynamic data-driven flight capability estimation. The main contribution of the work is a general mathematical approach that leverages offline vehicle analysis and design data together with onboard sensor measurements to achieve dynamic path planning. The mathematical framework, expressed as a Constrained Partially Observable Markov Decision Process, accounts for vehicle capability constraints and is robust to modeling error and disturbances in both the vehicle process and measurement models. Vehicle capability constraints are incorporated using Probabilistic Support Vector Machine surrogates of high-fidelity physics-based models that adequately capture the richness of the vehicle dynamics. Sensor measurements are treated in a general manner and can include combinations of multiple modalities such as GPS/IMU data as well as structural strain data of the airframe. Results are presented for a simulated 3-D environment and point-mass airplane model. The vehicle can dynamically adjust its trajectory according to the observations it receives about its current state of health, thereby retaining a high probability of survival and mission success.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2016-4124
