Evolving neural networks through augmenting topologies
Evolutionary Computation
Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics
Journal of Intelligent and Robotic Systems
A neuro-evolutionary approach to micro aerial vehicle control
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Quadrotors are unique among Micro Aerial Vehicles in providing excellent maneuverability (as opposed to winged flight), while maintaing a simple mechanical construction (as opposed to helicopters). This mechanical simplicity comes at a cost of increased controller complexity. Quadrotors are inherently unstable, and micro quadrotors are particularly difficult to control. In this paper, we evolve a hierarchical neuro-controller for micro (0.5 kg) quadrotor control. The first stage of control aims to stabilize the craft and outputs rotor speeds based on a requested attitude (pitch, roll, yaw, and vertical velocity). This controller is developed in four parts around each of the variables, and then combined and trained further to increase robustness. The second stage of control aims to achieve a requested (x, y, z) position by providing the first stage with the appropriate attitude. The results show that stable quadrotor control is achieved through this architecture. In addition, the results show that neuro-evolutionary control recovers from disturbances over an order of magnitude faster than a basic PID controller. Finally, the neuro-evolutionary controller provides stable flight in the presence of 5 times more sensor noise and 8 times more actuator noise as compared to the PID controller.