Global Path Planning for Full-area Coverage Robotic Systems by Employing an Active Genetic Algorithm

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made. This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms. The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.