In this paper the problem of path planning in a dynamic environment is considered. The minimum cost path is planned using Cellular Neural Network (CNN). CNN is a very useful tool for parallel signal processing and can be implemented using VLSI. In the proposed approach the problem of local minima (dead ends on a map) does not exist. Different criteria can be taken into account during path planning for example: the size of the robot, the traversability cost, the occurrence of dynamic obstacles, etc. The method allows us to specify the goal using semantic labels. The experiments performed in a real static environment and simulations in a dynamic environment have shown the efficiency of the proposed method. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Przybylski, M., & Siemiatkowska, B. (2012). A new CNN-based method of path planning in dynamic environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7268 LNAI, pp. 484–492). Springer Verlag. https://doi.org/10.1007/978-3-642-29350-4_58
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