Name
Title | ||
|---|---|---|
AGV decision making subsystem based on modified Dempster-Shafer evidence theory and fuzzy logic |
Abstract | ||
|---|---|---|
The AGV decision making subsystem directly affects the performance of the vehicle. The information it uses can be classified into "Objective Information" and "Subjective Information" two major groups. To fuse these two kinds of information, we propose a novel framework for decision in this paper. In the framework, an effective method based on the modified Dempster-Shafer evidence theory was used to make the fusion of the objective and subjective information. In addition, we used fuzzy logic to quantify the subjective information. The experiment shows the proposed method can solve the vagueness and uncertainty of information and achieve decision exactly and credibly. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICVES.2012.6294284 | ICVES |
Keywords | Field | DocType |
fuzzy logic,case based reasoning,mobile robots,cognition,sensors,fuses,sensor fusion,information classification | Computer vision,Vagueness,Linear partial information,Effective method,Fuzzy logic,Sensor fusion,Artificial intelligence,Engineering,Case-based reasoning,Dempster–Shafer theory,Machine learning,Mobile robot | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuqiang Liu | 1 | 0 | 0.34 |
| Wuling Huang | 2 | 0 | 0.34 |
| Tao Sun | 3 | 168 | 16.47 |
| Fenghua Zhu | 4 | 193 | 33.75 |