Browsing by Subject "Quadrotor"
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Item The autonomous guidance, navigation, and control laboratory at the University of Texas at Austin(2015-12) Lowery, Timothy Vernon; Açıkmeşe, Behçet; Akella, Maruthi RThis report details the design, construction, and contents of the Autonomous Guidance, Navigation, and Control Laboratory (AGNC Lab) for Dr. Behcet Acikmese at the University of Texas. It is intended as a resource for those who are new to the lab or to one of its systems. The lab was created to test --- on real-world platforms --- the control algorithms produced by Dr. Acikmese’s research group. To separate the control problems from other engineering challenges of autonomous vehicles, the lab uses an optical motion capture system which can relay vehicle's their position and orientation. To support hardware development, the lab houses a full compliment of hand tools, electronics equipment, and a 3D extrusion printer. The primary research vehicle is the quadrotor, selected for its mechanical simplicity, aerial agility, and recent ubiquity. Through the testing of several quadrotors, my group found existing platforms did not fulfill our need for small size and weight, outdoor flight, payload capacity, and computational power. In response, we designed a custom quadrotor and autopilot. The vehicle flies safely indoors, confidently outdoors, and with a payload of up to half its own mass. The autopilot is based on an ARM microprocessor, leaving ample overhead for our group's algorithms, and can easily add new functionality with breakout boards.Item Lossless convexification of quadrotor motion planning with experiments(2014-08) Pehlivantürk, Can; Longoria, Raul G.; Açıkmeşe, BehçetThis 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.