  An easily implementable path solution algorithm for 2D spatial problems,
based on excitable/programmable characteristics of a specific cellular
nonlinear network (CNN) model is presented and numerically investigated. The
network is a single layer bioinspired model which was also implemented in CMOS
technology. It exhibits excitable characteristics with regionally bistable
cells. The related response realizes propagations of trigger autowaves, where
the excitable mode can be globally preset and reset. It is shown that, obstacle
distributions in 2D space can also be directly mapped onto the coupled cell
array in the network. Combining these two features, the network model can serve
as the main block in a 2D path computing processor. The related algorithm and
configurations are numerically experimented with circuit level parameters and
performance estimations are also presented. The simplicity of the model also
allows alternative technology and device level implementation, which may become
critical in autonomous processor design of related micro or nanoscale robotic
applications.
