Name
Title | ||
|---|---|---|
Statistical identification and macroscopic transitional model between disorder and order |
Abstract | ||
|---|---|---|
Food processing provides a lot of possibilities to apply robotics and automation. In this paper, we identify disordered and ordered states of discrete food products. The concept of Degree of Disarray is introduced. Food ordering processes such as vibratory feeders, multi-head weighers, pick and place operations are common automation in food industry to transfer products from a higher to a lower Degree of Disarray. Parts entropy is introduced to describe a product's individual state based on the symmetry categorisation. A macroscopic transitional model is presented which determines a subspace of the disordered arrangement using the eigenvectors of the largest eigenvalues of the covariance matrix. A projection into this created subspace follows. As soon as the disorder state in only one dimension is achieved, the point of disorder can be derived which finally transfers the objects into order. From here, a transformation to any order arrangement in any dimension is possible. This methodology is applied to pick and place operations and experiments are conducted. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2014.6907462 | Robotics and Automation |
Keywords | Field | DocType |
covariance matrices,eigenvalues and eigenfunctions,food products,food technology,materials handling,principal component analysis,covariance matrix,degree-of-disarray concept,discrete food products,eigenvectors,food processing,macroscopic transitional model,multihead weighers,pick-and-place operations,statistical identification,symmetry categorisation,vibratory feeders | Econometrics,Control engineering,Mathematics,Calculus | Conference |
Volume | Issue | ISSN |
2014 | 1 | 1050-4729 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helge A. Wurdemann | 1 | 151 | 11.63 |
| Vahid Aminzadeh | 2 | 13 | 3.18 |
| Jian S. Dai | 3 | 178 | 30.53 |