Evolved Navigation Control for Unmanned Aerial Vehicles
نویسندگان
چکیده
Whether evolutionary robotics (ER) controllers evolve in simulation or on real robots, realworld performance is the true test of an evolved controller. Controllers must overcome the noise inherent in real environments to operate robots efficiently and safely. To prevent a poorly performing controller from damaging a vehicle—susceptible vehicles include statically unstable walking robots, flying vehicles, and underwater vehicles—it is necessary to test evolved controllers extensively in simulation before transferring them to real robots. In this paper, we present our approach to evolving behavioral navigation controllers for fixed wing unmanned aerial vehicles (UAVs) using multi-objective genetic programming (GP), choosing the most robust evolved controller, and assuring controller performance prior to real flight tests.
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