Optimal path finding with space- and time-variant metric weights via multi-layer CNN

Analogic CNN-based optimal path-finding algorithm is proposed to solve the problem with space- and time-variant metric weights. The algorithm is based on the analog version of modified dynamic programming which is associated with non-linear templates and multi-layer CNN employing the distance computing (DC), the intermediate (1), and the path-finding (PF) layers. The cell outputs of I layer are jointly utilized among the cells on the DC layer and the PF layers, which allows the network structure to be compact. The arbitrary levels of metric weights can be provided externally and the real-time processing of the optimal path finding is achieved on the space with the time-variant metric weight. Parallel-processing capability for the multiple optimal path finding is the additional property of the proposed algorithm. The proposed multi-layer CNN structure and its non-linear templates are introduced. The proper operation of the proposed structure is verified through theoretical analysis and simulations. Copyright (C) 2002 John Wiley Sons, Ltd.