Name
Abstract | ||
|---|---|---|
Inspired by the mechanism of Jerne’s idiotypic network hypothesis, a new adaptive immune network algorithm (AINA) is presented
through the stimulation and suppression between the antigen and antibody by taking the environment and robot behavior as antigen
and antibody respectively. A guiding weight is defined based on the artificial potential field (APF) method, and the guiding
weight is combined with antibody vitality to construct a new antibody selection operator, which improves the searching efficiency.
In addition, an updating operator of antibody vi-tality is provided based on the Baldwin effect, which results in a positive
feedback mechanism of search and accelerates the convergence of the immune network. The simulation and experimental results
show that the proposed algorithm is characterized by high searching speed, good convergence performance and strong planning
ability, which solves the path planning well in complicated environments. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11704-009-0015-5 | Frontiers of Computer Science in China |
Keywords | Field | DocType |
artificial potential field,immune network,path planning,Baldwin effect | Motion planning,Convergence (routing),Immune network,Antigen,Computer science,Robot path planning,Algorithm,Operator (computer programming),Artificial intelligence,Behavior-based robotics,Machine learning,Baldwin effect | Journal |
Volume | Issue | ISSN |
3 | 2 | 16737466 |
Citations | PageRank | References |
1 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingxin Yuan | 1 | 7 | 3.19 |
| Sunan Wang | 2 | 38 | 10.17 |
| Canyang Wu | 3 | 5 | 1.10 |
| Kunpeng Li | 4 | 51 | 6.37 |