Name
Abstract | ||
|---|---|---|
The concept of resolution-exact path planning is a theoretically sound alternative to the standard exact algorithms, and provides much stronger guarantees than probabilistic or sampling algorithms. It opens the way for the introduction of soft predicates in the context of subdivision algorithm. Taking a leaf from the great success of the Probabilistic Road Map (PRM) framework, we formulate an analogous framework for subdivision, called Soft Subdivision Search (SSS). In this video, we illustrate the SSS framework for a trio of simple planar robots: disc, triangle and 2-links. These robots have, respectively, 2, 3 and 4 degrees of freedom. Our 2-link robot can also avoid self-crossing. These algorithms operate in realtime and are relatively easy to implement. |
Year | Venue | Field |
|---|---|---|
2016 | Symposium on Computational Geometry | Motion planning,Computer science,Road map,Algorithm,Subdivision,Probabilistic logic,Subdivision algorithms,Robot,Gibbs sampling,Configuration space |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Hsiang Hsu | 1 | 0 | 1.35 |
| John Paul Ryan | 2 | 0 | 0.34 |
| Chee-Keng Yap | 3 | 1996 | 395.32 |