An efficient Genetic Algorithm for the global robot path planning problem

Path planning problem (PPP) is a challenging problem in several real-life applications and needs to be developed and implemented in a suitable manner. For that purpose we designed an efficient Genetic Algorithm (GA) for modeling and solving PPP. The proposed GA exploits an adequate solution representation and a smart crossover operator, and introduces a significant fitness function involving several criteria of robot motion. We conducted a comprehensive experiments of the proposed algorithm and we compared it against two different approaches of the literature: Genetic Algorithm and Tabu search algorithms. Simulation results show the efficiency and effectiveness of our genetic algorithm in comparison with the other planners in term of solution quality and execution time.