Name
Abstract | ||
|---|---|---|
Efficient motion planning for robots with many degrees of freedom requires the exploration of a large configuration space. Sampling based motion planners perform approximate exploration of the configuration space in order to render the problem tractable. Each sample of configuration space as an opportunity to gain information about that configuration space. A formal definition of information gain can be used to guide a motion planner to achieve maximal progress toward the discovery of a path. We call such a motion planner entropy-guided since entropy reduction is synonymous with information gain. In the following we describe a single-query entropy-guided motion planner which uses a formal definition of information gain to focus its efforts on the acquisition of a single path from start to goal locations. Experimental evidence indicates that this approach can outperform existing single-query techniques. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ROBOT.2005.1570427 | ICRA |
Keywords | Field | DocType |
configuration space,entropy,degree of freedom,path planning,motion planning,space exploration,sampling methods,information theory,computer science | Any-angle path planning,Planner,Theoretical computer science,Control engineering,Space exploration,Artificial intelligence,Motion planning,Entropy reduction,Sampling (statistics),Robot,Mathematics,Machine learning,Configuration space | Conference |
Volume | Issue | ISSN |
2005 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
0-7803-8914-X | 4 | 0.49 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brendan Burns | 1 | 304 | 27.70 |
| Oliver Brock | 2 | 154 | 14.61 |