Ultrasonic sensing based robot localization using entropy nets

It is pointed out that the most attractive feature of artificial neural networks is the procedural nature of learning that allows the capturing of the mapping present in the input-output data without the need for extensive model building. The authors exploit this feature to solve the task of robot localization using ultrasonic sensors. The localization problem is casted as a regression problem which is then solved by using a feedforward-type multiple-layer neural network. The network design and training are done following the entropy net model that uses a tree to network mapping to obtain the network of appropriate size. A representation layer is added to improve output accuracy. Experimental results for the simulated and real data are presented to demonstrate the performance of the proposed approach