Associative memory implementation in path-planning for mobile robots

A solution to the path-planning problem for mobile robots is proposed. The solution is based on a geometrical model, a logical model, and an associative memory. The geometrical modeling is done by a graph produced by analog-mapping preference gangways in the environment. The path search is made by a best-first algorithm in the logical model. The costs of the individual graph arcs are dynamically updated. During the search, the associative memory is repeatedly consulted for an available path. When a valid path is found it is learned by the memory for later use. For the memory, the path is tristate-encoded and preprocessed by the graph connection matrix. An example is given to illustrate the operation of the associative memory