SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A framework for modeling steady turning of robotic fish
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Crossing the reality gap in evolutionary robotics by promoting transferable controllers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
On-line, on-board evolution of robot controllers
EA'09 Proceedings of the 9th international conference on Artificial evolution
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Evolving flexible joint morphologies
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Applying evolutionary computation to harness passive material properties in robots
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Developing complex behaviors for aquatic robots is a difficult en- gineering challenge due to the uncertainty of an underwater environment. Neuroevolution provides one method of dealing with this type of problem. Artificial neural networks discern different conditions by mapping sensory input to responses, and evolutionary computation provides a training algorithm suitable to the high dimensionality of the problem. In this paper, we present results of applying neuroevolution to an aquatic robot tasked with station keeping, that is, maintaining a given position despite surrounding water flow. The virtual device exposed to evolution is modeled af- ter a physical counterpart that has been fabricated with a 3D printer and tested in physical environments. Evolved behaviors exhibit a variety of unexpected, complex fin/flipper movements that enable the robot to achieve and maintain station, despite water flow from different directions. Moreover, the results show that evolved controllers are able to effectively carry out this task using only infor- mation from a simulated accelerometer and gyroscope, matching the inertial measurement unit (IMU) on the actual robot.