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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@MastersThesis{barlow2004-thesis,

• author = "Gregory J. Barlow",
• title = "Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming",
• school = "North Carolina State University",
• year = "2004",
• address = "Raleigh, NC, USA",
• month = mar,
• keywords = "genetic algorithms, genetic programming, mobile robotics, evolutionary robotics, multi-objective optimization, incremental evolution, unmanned aerial vehicles",
• URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/thesis/barlow2004-thesis/barlow2004-thesis.pdf",
• size = "181 pages",
• abstract = "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 efficiently 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. All computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types. Both the direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the success rate of incremental evolution over direct evolution. The final incremental evolution experiment on the most complex radar investigated in this research evolved controllers that were able to handle all of the radar types. Evolved UAV controllers were successfully transferred to a wheeled mobile robot. An acoustic array on-board the mobile robot replaced the radar sensor, and a speaker emitting a tone was used as the target. Using the evolved navigation controllers, the mobile robot moved to the speaker and circled around it. Future research will include testing the best evolved controllers by using them to fly real UAVs.",
• notes = "ADA460111",

}

Genetic Programming entries for Gregory J Barlow

Citations