Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-29350-4_58 | ICAISC (2) |
Keywords | Field | DocType |
traversability cost,real static environment,cellular neural network,dynamic obstacle,path planning,new cnn-based method,dynamic environment,minimum cost path,dead end | Motion planning,Signal processing,Any-angle path planning,Computer science,Maxima and minima,Artificial intelligence,Mobile robot navigation,Robot,Cellular neural network,Very-large-scale integration,Machine learning | Conference |
Volume | ISSN | Citations |
7268 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maciej Przybylski | 1 | 0 | 0.68 |
| Barbara Siemiatkowska | 2 | 21 | 2.57 |