Name
Abstract | ||
|---|---|---|
In this paper, a merged convolution neural network (CNN) framework is proposed to automatically avoid obstacles. Although there are many methods for avoiding obstacles, previous methods mostly contain high energy-consuming and high cost. This paper aims to realize an image-based method with a monocular webcam. The experimental results illustrate that the proposed method can effectively avoid obstacles in mobile robot navigation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMLC48188.2019.8949168 | PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC) |
Keywords | Field | DocType |
Obstacle avoidance method, Merged CNN framework, ROS architecture | Obstacle avoidance,Computer vision,Pattern recognition,Convolutional neural network,Computer science,Artificial intelligence,Mobile robot navigation,Robot,Monocular | Conference |
ISSN | Citations | PageRank |
2160-133X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nai-Hsiang Chang | 1 | 0 | 0.34 |
| Yi-Hsing Chien | 2 | 89 | 8.33 |
| Hsin-Han Chiang | 3 | 0 | 0.68 |
| Wei-Yen Wang | 4 | 995 | 87.40 |
| Chen-Chien James Hsu | 5 | 38 | 11.17 |