Name
Abstract | ||
|---|---|---|
This paper presents a path-following system implemented with two different types of neural networks, that enables an autonomous
mobile robot to return along a previously learned path in a dynamic environment. The path-following is based on data provided
by an omnidirectional conical visual system, derived from the COPIS sensor, but with different optical reflective properties.
The system uses optical and software processing and a neural network to learn the path, described as a sequence of selected
points. In the navigation phase it drives the robot along this learned path. Interesting results have been achieved using
low cost equipment. Test and results are presented. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/s005210200015 | Neural Computing and Applications |
Keywords | DocType | Volume |
Key words: Neural networks,Omnidirectional Vision,Robotic navigation | Journal | 11 |
Issue | ISSN | Citations |
1 | 0941-0643 | 1 |
PageRank | References | Authors |
0.36 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Rizzi | 1 | 605 | 64.43 |
| Riccardo Cassinis | 2 | 21 | 4.20 |
| N. Serana | 3 | 1 | 0.36 |