Name
Abstract | ||
|---|---|---|
We introduce a Distributed Model Predictive Control (DMPC) algorithm, which is based on the novel idea of projecting predicted trajectories on a quantised spatial set to reduce the communication load. The scheme exploits advantages of continuous optimisation methods while only quantised data is broadcasted. Further, we set up a differential communication scheme, in which only altered cells instead of the full prediction are broadcasted. While the quantisation reduces the communication effort for the overall system, the differential communication further reduces the effort depending on the chosen cell size. The approach is evaluated in simulations using groups of holonomic and non-holonomic mobile robots. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2018.04.034 | Information Sciences |
Keywords | Field | DocType |
Distributed Model Predictive Control (DMPC),Non-holonomic and holonomic robots,Multi-agent system,Quantisation,Collision avoidance | Holonomic,Algorithm,Exploit,Artificial intelligence,Distributed model predictive control,Mathematics,Mobile robot,Machine learning,Occupancy grid mapping | Journal |
Volume | ISSN | Citations |
453 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 17 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Sprodowski | 1 | 0 | 2.37 |
| Mohamed W. Mehrez | 2 | 14 | 3.04 |
| Karl Worthmann | 3 | 118 | 14.22 |
| George K. I. Mann | 4 | 425 | 39.50 |
| Raymond G. Gosine | 5 | 252 | 24.37 |
| Juliana Keiko Sagawa | 6 | 11 | 2.01 |
| Jürgen Pannek | 7 | 66 | 19.51 |