Path Planning of Mobile Robots Using Enhanced Particle Swarm Optimization

With the rapid growth of technology and extensive application of robots, autonomous mobile robots have gained a lot of attention in industry and research. One of the crucial issues is to find out a collision-free path through which robots can reach the destination. In this paper, evolutionary optimization based autonomous path planning approach for mobile robots is proposed. We have introduced an adaptive fitness function which takes care of three crucial aspects such as (i) avoidance of obstacles in the path (ii) selection of shorter path length and (iii) selection of smoother path of the path planning process. Particle swarm optimization (PSO) algorithm is used to optimize the fitness function. Optimization of the objective function via suggested fitness function enables to generate a smoother and collision free path from the initial position to the destination position in presence of various obstacles. Extensive simulation experiments are performed to validate the performance of our proposed work. Performance behavior of our proposed work is compared with some of the existing state-of-the-art optimization based path planning methods and found to produce superior results.