Name
Abstract | ||
|---|---|---|
In this paper, we have projected an efficient mining method for a temporal dataset of humanoid robot HOAP-2 (Humanoid Open Architecture Platform). This method is adequate to discover knowledge of intermediate patterns which are hidden inside different existing patterns of motion of HOAP-2 joints. Pattern-growth method such as FP (Frequent Pattern) growth, unfolds many unpredictable associations among different joint trajectories of HOAP-2 that can depict various kinds of motion. In addition, we have cross-checked our methodology over Webots, a simulation platform for HOAP-2, and found that our investigation is adjuvant to predict new patterns of motion in terms of temporal association rules for HOAP-2. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10646-0_43 | RSFDGrC |
Keywords | Field | DocType |
humanoid robot,hoap-2 joint,different existing pattern,different joint trajectory,humanoid open architecture platform,efficient mining method,pattern-growth method,frequent pattern,temporal dataset,pattern growth method,temporal association rule,mining temporal patterns,humanoid robot hoap-2,association rule,open architecture | Open architecture,Computer science,Association rule learning,Artificial intelligence,Machine learning,Humanoid robot | Conference |
Volume | ISSN | Citations |
5908 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Upasna Singh | 1 | 1 | 0.70 |
| Kevindra Pal Singh | 2 | 1 | 0.36 |
| G. C. Nandi | 3 | 71 | 10.28 |