Name
Abstract | ||
|---|---|---|
By the number of people aged 60 or over and people with disabilities growing, homecare mobile robot draws increasing attention. However, there are challenges of autonomous navigation for homecare robot such as frequent changes of environment, obstacles and goal position. In this paper, we focus on verifying potential of neural network-based autonomous navigation for homecare mobile. And we compare recurrent neural network with multilayer perceptron in the navigation of an autonomous mobile robot. The result suggested that the recurrent neural network can do better robot navigation because of its capability to handle the temporal dependency of a data sequence. Also, it shows that neural network-based navigation can be a good alternative since it has decent generalization ability for new environment, obstacles and goals. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIGCOMP.2017.7881744 | 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | Field | DocType |
neural network,autonomous navigation,homecare mobile robot | Computer vision,Robot control,Social robot,Computer science,Recurrent neural network,Multilayer perceptron,Artificial intelligence,Mobile robot navigation,Artificial neural network,Robot,Mobile robot | Conference |
ISSN | ISBN | Citations |
2375-933X | 978-1-5090-3016-3 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ByungSoo Ko | 1 | 3 | 1.76 |
| Ho-Jin Choi | 2 | 280 | 53.61 |
| Chansol Hong | 3 | 2 | 0.76 |
| Jong-Hwan Kim | 4 | 8 | 6.01 |
| Oh Chul Kwon | 5 | 0 | 0.34 |
| Chang D. Yoo | 6 | 375 | 45.88 |