Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-Objective Genetic Programming

Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the fi...

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Bibliographic Details
Main Author: Barlow, Gregory J
Format: Report
Language:English
Subjects:
AUTONOMOUS NAVIGATION
CONTROL SYSTEMS
DRONES
EC(EVOLUTIONARY COMPUTATION)
ER(EVOLUTIONARY ROBOTICS)
EVOLUTION(GENERAL)
FIXED WING AIRCRAFT
FLIGHT SIMULATION
GP(GENETIC PROGRAMMING)
Pilotless Aircraft
ROBOTICS
UAVS(UNMANNED AERIAL VEHICLES)
UNMANNED
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Summary:Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution. In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications. Four behavioral fitness functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy source, to navigate the UAV to that source ef ciently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies.