Name
Abstract | ||
|---|---|---|
In order to achieve fast motion planning for a robot we have chosen the genetic algorithms for the following reasons: - they are well adapted to search for solutions in high dimensionality search space. The algorithm can be used without reduction of its efficiency for arms with more than six degree of freedom, - they are very tolerant to the form of the function to optimize, for instance these functions do not need to be neither differentiable or continuous. They make no assumptions about the problem space that they are searching. We are using them to solve other optimization problems: graph partitioning, quadratic assignment, ... - they are easy to implement on massively parallel distributed memory architectures. The parallel algorithm proposed achieve near-linear speed-up. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1007/3-540-55895-0_513 | CONPAR |
Keywords | Field | DocType |
parallel robot motion planning,dynamic environment,motion planning,graph partitioning,parallel algorithm,parallel robot,search space,optimization problem,degree of freedom | Motion planning,Parallel manipulator,Mathematical optimization,Massively parallel,Parallel algorithm,Computer science,Distributed memory,Graph partition,Optimization problem,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
634 | 0302-9743 | 3-540-55895-0 |
Citations | PageRank | References |
2 | 0.38 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| El-Ghazali Talbi | 1 | 2186 | 179.85 |
| Pierre Bessière | 2 | 425 | 86.40 |
| Juan Manuel Ahuactzin | 3 | 84 | 25.07 |
| E. Mazer | 4 | 2 | 0.38 |