Two mobile robots sharing topographical knowledge generated by the region-feature neural network

This paper documents how two mobile robots can share knowledge about their environment. The two mobile robots we use have different sensor configurations, drive systems and other physical attributes including weight and size. “Knowledge” is generated by the region-feature neural network (RFNN), and can be transferred on two general levels: (1) a complete transfer of a matured neural network; and (2) a transfer of matured features. This transferred knowledge can also be “tuned” with and without locking the feature level synaptic weights. We examine the impact and feasibility of sharing (on both levels, with and without locking features) neural network modules trained on actual sonar data in the global self-localization (GSL) problem. Significant reductions in training time are realized and presented. We also describe the neural network architecture and our general approach to solving the GSL problem in a time-, translation- and rotation-invariant way