Evolving Robot Arm Controllers Using the NEAT Neuroevolution Method (2006)
Neuroevolution can be used to evolve neural networks that can control robot manipulators to perform tasks like target tracking and obstacle avoidance in complex environments. Neurocontrollers have been successful in the robot control domain because they are robust to noise, they can be adapted to different environments and manipulator configurations, and they can be used to implement controllers that can perform online learning.

The focus of this report was to evolve neurocontrollers for two environments. First, neurocontrollers are evolved for environments without obstacles and their performance is compared to an inverse kinematic controller and a potential field controller. Second, neurocontrollers are evolved for environments with obstacles and compared with a controller that uses potential fields to implement a path planning algorithm. The neurocontrollers evolved in this report come close to matching the performance of the analytical controllers. The advantage of using neurocontrollers is their robustness to noise and ability to adapt to different environments.

Masters Thesis, Department of Electrical and Computer Engineering, The University of Texas at Austin.

Thomas D'Silva Masters Alumni twdsilva [at] gmail com
NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010