Optimal Path Planning For Intelligent Mobile Robot Navigation Using Modified Particle Swarm Optimization

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. Using the heuristic approach, the mobile robot can navigate safely among the obstacles without hitting them and reach the predefined target point. These techniques are also helpful for the solution of the local minima problem. Researchers have been seeking for more efficient ways to solve this problem, in the following section, the recent works on robot's navigation and path planning using particle swarm are reviewed. (4) provided an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been discussed, and showed that the evolutionary optimization algorithms are computationally efficient. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed. (5) proposed a modified particle swarm optimization algorithm for the robot path planning in dynamic environment, two parameters of particle-distribution-degree and particle dimension-distance are introduced into the proposed algorithm in order to avoid premature convergence. (6) provided an intelligent approach for the navigation of a mobile robot in unknown environments, the navigation problem becomes an optimization problem, and then it is solved by PSO algorithm. Based on position of goal, an evaluation function for every particle in PSO is calculated. It's assumed that Robot can detect only obstacles in a limited radius of surrounding with its sensors. Environment is supposed to be dynamic and obstacles can be fixed or movable. (7) proposed an Immune Particle Swarm Optimization (IPSO) algorithm for path planning of the mobile robot which based on the biological mechanism of the immune system. They compared the simulation results with PSO optimization results. They concluded that the optimal path and the execution time based on IPSO algorithm are reduced separately, and the improved PSO algorithm enhances the convergence speed and robustness of time-varying parameters. From the above literature review for the recently published paper, it is concluded that various works of research have been successfully applied PSO to solve the mobile robot path planning problem, due to its simplicity and efficiency in navigating large search spaces for optimal solutions.