A mobile robot path planning using Genetic Artificial Immune Network algorithm

In this paper, the problem of finding the optimal collision free path, path planning for the case of a controllable mobile robot moving in a static environment filled with obstacles with known shape and size is studied. A path planner based on a hybrid memetic algorithm, genetic artificial immune network (GAIN), which provides near optimal collision free path is proposed. Genetic artificial immune network is a hybrid memetic algorithm based on genetic algorithm (GA) and artificial immune network (AIN) algorithm. The network cell structures are simple which makes the operators simple and results in a fast calculation with smaller number of cells. The results obtained from GAIN is compared with that of GA and GAIN is found to outperform GA in terms of convergence speed and result obtained, making it a promising algorithm for solving the mobile robot path planning problem.