Online Interactive Neuro-evolution
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Distributed optimization and flight control using collectives
Distributed optimization and flight control using collectives
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Distributed agent-based air traffic flow management
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Robust neuro-control for a micro quadrotor
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Applying classical control methods to Micro Aerial Vehicles (MAVs) is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. Such methods rely heavily on difficult to obtain models and are particularly ill-suited to the stochastic and dynamic environments in which MAVs operate. Instead, in this paper, we focus on a neuro-evolutionary method that learns to map MAV states (position, velocity) to MAV actions (e.g., actuator position). Our results show significant improvements in response times to minor altitude and heading corrections over a traditional PID controller. In addition, we show that the MAV response to maintaining altitude in the presence of wind gusts improves by a factor of five. Similarly, we show that the MAV response to maintaining heading in the presence of turbulence improves by factors of three.