A hybrid metaheuristic (genetic algorithm)-PSO algorithm have presented toward find an optimal path among a preliminary point along with the end point in a grid environment for autonomous robot navigation is presented. GA is combined with PSO to develop new solutions through the use of particle-built solutions by crossover and mutation operators. The use of cubic B-spline techniques to create nearly optimal collision free continuous path Simulation of conventional GA and PSO algorithms prevents premature convergence and time complexity. This hybrid algorithm avoids The initial feasible path generate from GA-PSO hybrid planner results be taken with purpose to demonstrate the value of the proposed GA-PSO hybrid road planner and to plan the autonomous robotic path more smoothly.