Lossless convexification of quadrotor motion planning with experiments
This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrot...
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ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-263882015-09-20T17:26:49ZLossless convexification of quadrotor motion planning with experimentsPehlivantürk, CanQuadrotorUAVTrajectory generationConvexificationThis thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller.text2014-10-09T16:18:38Z2014-082014-09-04August 20142014-10-09T16:18:38ZThesisapplication/pdfhttp://hdl.handle.net/2152/26388en |
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Quadrotor UAV Trajectory generation Convexification |
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Quadrotor UAV Trajectory generation Convexification Pehlivantürk, Can Lossless convexification of quadrotor motion planning with experiments |
description |
This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller. === text |
author |
Pehlivantürk, Can |
author_facet |
Pehlivantürk, Can |
author_sort |
Pehlivantürk, Can |
title |
Lossless convexification of quadrotor motion planning with experiments |
title_short |
Lossless convexification of quadrotor motion planning with experiments |
title_full |
Lossless convexification of quadrotor motion planning with experiments |
title_fullStr |
Lossless convexification of quadrotor motion planning with experiments |
title_full_unstemmed |
Lossless convexification of quadrotor motion planning with experiments |
title_sort |
lossless convexification of quadrotor motion planning with experiments |
publishDate |
2014 |
url |
http://hdl.handle.net/2152/26388 |
work_keys_str_mv |
AT pehlivanturkcan losslessconvexificationofquadrotormotionplanningwithexperiments |
_version_ |
1716824029427400704 |