Abstract- This study investigates the application of Modified

Particle Swarm Optimization (MPSO) to the problem of mobile robot navigation to determine the shortest feasible path with the minimum time required to move from a starting position to a target position in working environment with obstacles. In this study, MPSO is developed to increase the capability of the optimized algorithms for a global path planning. The proposed algorithms read the map of the environment which expressed by grid model and then creates an optimal or near optimal collision free path. The effectiveness of the proposed optimized algorithm for mobile robot path planning is demonstrated by simulation studies. The programs are written in MATLAB R2012a and run on a computer with 2.5 GHz Intel Core i5 and 6 GB RAM. Improvements presented in MPSO are mainly trying to address the problem of premature convergence associated with the original PSO. In the MPSO an error factor is modelled to ensure the PSO converges. MPSO try to address another problem which is the population may contain many infeasible paths; a modified procedure is carrying out in the MPSO to solve the infeasible path problem. The results demonstrate that this algorithm have a great potential to solve the path planning with satisfactory results in terms of minimizing distance and execution time