A sensorimotor controller has been implemented to enable a mobile robot to learn its motion control autonomously and perform simple target-reaching movements. This controller is able to perform fine motion by reducing its self-positioning error and also, reach a designated target location with minimum delay. The control architecture is in the form of a neural network known as the Self-Organizing Map. Besides implementing the motor control and the online learning algorithms, the essentiality of a pre-learning phase is also evaluated. Then, we explore the possibility of incorporating a novel concept known as Local Linear Smoothing into our batch training algorithm; this notion allows the elimination of the boundary bias phenomenon. Lastly, we suggest a simple approach to learning in an obstacle-ridden environment
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