A distributed robotic control system based on a temporal self-organizing neural network

A distributed robot control system is proposed based on a temporal self-organizing neural network, called a competitive temporal Hebbian (CTH) network. The CTH network can learn and recall complex trajectories using two sets of synaptic weights, namely competitive feedforward weights that encode the individual states of the trajectory and Hebbian lateral weights that encode the temporal order of the trajectory states. Ambiguities that occur during trajectory reproduction are resolved using temporal context information. Also, the CTH network saves memory space by maintaining only a single copy of each repeated/shared state of a complex trajectory. A distributed processing scheme is proposed to evaluate the CTH network in the point-to-point real-time trajectory control of a Puma 560 robot. The performance of the control system is discussed and compared with other neural network approaches