Abstract
A Novel Cellular Neural Network (CNN) entitled the shortest path CNN (SP-CNN) is proposed in this paper. Compared with general CNN, it is distinguished in the network structure and neural dynamics. As a result of these distinctions, SP-CNN has a good performance in motion planning for mobile robots. By mapping environment information to parameters in this neural network, motion planning can be transformed to the state evolvement of SP-CNN and the generated state represents information of the optimal path. The proposed method generates the best solution in static environments in real time. Extensive simulations about the above mentioned aspects demonstrate the effectiveness of the proposed approach.
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