A novel immune-genetic based algorithm for mobile robot´s path planning

In this study we present our idea for using immune-genetic algorithm with the elitism and an improved roulette wheel selection strategy (IGAE-R) to help a controllable mobile robot to find an optimal path between a starting and ending point in a static environment. Grid theory is utilized to establish the free space model of the monile robot in the environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. Some characteristics of IGAE-R include: immune-genetic with elitist reservation strategy, deletion and insertion operators during the searching process, and roulette wheel selection strategy. IGAE-R allows eight-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.