Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming
Gregory J. Barlow

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.