Neuroevolution of augmenting topologies based musculor-skeletal arm neurocontroller

Human-like musculor-skeletal arm model with its unique smooth and natural movements as well as low energy cost has attracted many researchers' interests and developed rapidly nowadays. In this paper, we focus on a human-like musculor-skeletal arm model with three links driven by nine muscles and propose a control model with two neurocontrollers trained by the NEAT algorithm. The NEAT (Neuroevolution of Augmenting Topologies) is a novel neuroevolution algorithm using a modified genetic algorithm to train neural networks by changing the topology structure as well as connection weights simultaneously. To evolve the network, we generate the training sets by using forward kinematics, geometry relationships, and muscle mechanic equations. By using two neurocontrollers, Position-Angle and Angle-Activation, the control model can deal with the redundancy and non-linear problems in separate neurocontroller at the same time.