Name
Abstract | ||
|---|---|---|
Many mobile robot algorithms require tedious tuning of parameters and are, then, often suitable to only a limited number of situations. Yet, as mobile robots are to be employed in various fields from industrial settings to our private homes, changes in the environment will occur frequently. Organic computing principles such as self-organization, self-adaptation, or self-healing can provide solutions to react to new situations, e.g. provide fault tolerance. We therefore propose a biologically inspired self-adaptation scheme to enable complex algorithms to adapt to different environments. The proposed scheme is implemented using the Organic Robot Control Architecture (ORCA) and Learning Classifier Tables (LCT). Preliminary experiments are performed using a graph-based Visual Simultaneous Localization and Mapping (SLAM) algorithm and a publicly available benchmark set, showing improvements in terms of runtime and accuracy. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-36424-2_20 | ARCS |
Keywords | Field | DocType |
complex algorithm,self-adaptation scheme,learning classifier tables,mobile robot algorithm,mobile robot,proposed scheme,organic computing principle,different environment,available benchmark set,fault tolerance,organic robot control architecture | Robot control,Architecture,Computer science,Algorithm,Field-programmable gate array,Fault tolerance,Organic computing,Simultaneous localization and mapping,Classifier (linguistics),Mobile robot | Conference |
Volume | ISSN | Citations |
7767 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Hartmann | 1 | 0 | 0.34 |
| Walter Stechele | 2 | 365 | 52.77 |
| Erik Maehle | 3 | 676 | 130.34 |