Abstract
In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.
| Original language | English |
|---|---|
| Pages (from-to) | 1982-1993 |
| Number of pages | 12 |
| Journal | International Journal of Systems Science |
| Volume | 46 |
| Issue number | 11 |
| Early online date | 9 Oct 2013 |
| DOIs | |
| Publication status | Published - 18 Aug 2015 |
Keywords
- evolutionary algorithm
- genetic algorithm
- global path planning
- memetic algorithm
- mobile robot
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications