Guidance laws for partially-observable UAV interception based on linear covariance analysis
Name
1057725527-MIT.pdf
Description
Full printable version
Size
7.5 MB
Format
Adobe PDF
Checksum (MD5)
6aa3b76d2a64bbfb158d6678288c8565
Author(s)
Arneberg, Jasper Thomas
Advisor(s)
Sertac Karaman and Gian Luca Mariottini.
Alternative Title
Guidance laws for partially-observable Unmanned Aerial Vehicle interception based on linear covariance analysis
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Unmanned Aerial Vehicles (UAVs) have proliferated the skies in recent years as they have become extremely popular for all different kinds of commercial, government, and recreational usage. With all this activity, there remains an open security threat, particularly to airports, soldiers, and large crowds of people. This thesis work is motivated by the idea of an autonomous pursuer drone that can intercept and capture a malevolent drone. Due to the limited payload of drones, we consider pursuit-evasion games characterized by partial state observability. Specifically, we consider bearing-only measurements, as can easily be obtained from a single camera sensor. In this work, an optimal control formulation for a drone pursuit-evasion game is achieved in 7 states. Using the sophisticated Continuous Computation and Compression (C3) library, a new optimal controller is calculated in compressed tensor train (TT) format. By compressing the state space, it is possible to calculate the optimal control action at any state in real time. A set of observability maneuvers is identified to help the pursuer improve the estimate quality of an Unscented Kalman Filter (UKF) tracking the target's relative position and velocity. Using Linear Covariance Analysis, an novel algorithm is developed to pick the series of maneuvers that gives the best probability of capture. This algorithm is demonstrated on a quadrotor in flight intercepting a simulated evader drone, and it is shown to improve the tracking performance error by several orders of magnitude.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 103-106).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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