Autonomous Vehicle Technologies for Small Fixed Wing UAVs

Autonomous Vehicle Technologies for Small Fixed Wing UAVs (2003)

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by Derek Kingston , Randal Beard , Al Beard , Timothy McLain , Michael Larsen , Wei Ren

Venue:	AIAA Journal of Aerospace Computing, Information, and Communication
Citations:	32 - 13 self

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Datum	Value	Source
TITLE	Autonomous Vehicle Technologies for Small Fixed Wing UAVs	user correction - Legacy Corrections
AUTHOR NAME	Derek Kingston	user correction - Legacy Corrections
AUTHOR AFFIL	1 Electrical and Computer Engineering; 2 Mechanical Engineering	user correction - Legacy Corrections
AUTHOR ADDR	Brigham Young University,; Provo, UT 84602	user correction - Legacy Corrections
AUTHOR NAME	Randal Beard	user correction - Legacy Corrections
AUTHOR NAME	Al Beard	user correction - Legacy Corrections
AUTHOR NAME	Timothy McLain	user correction - Legacy Corrections
AUTHOR NAME	Michael Larsen	user correction - Legacy Corrections
AUTHOR NAME	Wei Ren	user correction - Legacy Corrections
AUTHOR AFFIL	Copyright c○2003 American Institute of Aeronautics	user correction - Legacy Corrections
ABSTRACT	Autonomous unmanned air vehicle flight control systems require robust path generation to account for terrain obstructions, weather, and moving threats such as radar, jammers, and unfriendly aircraft. In this paper, we outline a feasible, hierarchal approach for real-time motion planning of small autonomous fixed-wing UAVs. The approach divides the trajectory generation into four tasks: waypoint path planning, dynamic trajectory smoothing, trajectory tracking, and low-level autopilot compensation. The waypoint path planner determines the vehicle 's route without regard for the dynamic constraints of the vehicle. This results in a significant reduction in the path search space, enabling the generation of complicated paths that account for pop-up and dynamically moving threats. Kinematic constraints are satisfied using a trajectory smoother which has the same kinematic structure as the physical vehicle. The third step of the approach uses a novel tracking algorithm to generate a feasible state trajectory that can be followed by a standard autopilot. Monte-Carlo simulations were done to analyze the performance and feasibility of the approach and determine real-time computation requirements. A planar version of the algorithm has also been implemented and tested in a low-cost micro-controller. The paper describes a custom UAV built to test the algorithms.	user correction - Legacy Corrections
YEAR	2003	INFERENCE
VENUE	AIAA Journal of Aerospace Computing, Information, and Communication	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2003--6559	INFERENCE
VOLUME	2	INFERENCE
CITATIONS	34 found	ParsCit 1.0