Name
Abstract | ||
|---|---|---|
Probabilistic roadmap methods (PRMs) are a commonly used approach to path planning problems in a high-dimensional search space. Although PRMs can often find a solution to solving the path finding problem the solutions are often not practical in that they can cause the device to flail around or to pass very close to obstacles in the environment. This paper presents a variant of PRMs that addresses the practicality problem of the paths found by the planner. A simple and general sample adjustment method is developed, which adjusts the randomly generated nodes that make up the PRM within their local neighborhood to satisfy soft constraints required by the problem. The resulting roadmap can then be used to generate more practical paths. The approach is general and can be adapted to path planning problems with different practical requirements. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CRV.2011.21 | CRV |
Keywords | Field | DocType |
sample adjustment method,path planning problem,general sample adjustment method,path planning problems,high-dimensional search space,probabilistic roadmap method,prm,resulting roadmap,mobile robots,soft constraints,practical paths,local neighborhood,path finding problem,path planning,practicality problem,robot path planning,soft constraint,search space,practicality-based probabilistic roadmaps method,different practical requirement,practical path,probability,path finding,satisfiability,probabilistic logic | Motion planning,Any-angle path planning,Mathematical optimization,Computer science,Probabilistic roadmaps,Robot path planning,Planner,Probabilistic roadmap,Fast path,Mobile robot | Conference |
ISBN | Citations | PageRank |
978-0-7695-4362-8 | 1 | 0.36 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Yang | 1 | 1 | 5.43 |
| Patrick Dymond | 2 | 18 | 2.67 |
| Michael Jenkin | 3 | 29 | 4.88 |