An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks

Self-organizing feature maps such as Extended KohonenMaps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This paper presents a new ensemble approach called cooperative EKMs with indirect mapping to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than discrete, motor control space. It is also shown to outperform basis function neural networks. Furthermore, training its control parameters with recursive least squares enables faster convergence and better performance compared to gradient descent. The cooperation and competition of multiple self-organized EKMs allow a non-holonomic mobile robot to negotiate unforeseen, concave, closely spaced, and dynamic obstacles. Qualitative and quantitative comparisons with neural network ensembles employing weighted sum reveal that our method can achieve more sophisticated