Comparison and analysis of obstacle avoiding path planning of mobile robot by using ant colony optimization and teaching learning based optimization techniques

Abstract

Now a day, one of the prime concerns of mobile robot is path planning, in the area of industrial robotics. A path planning optimization method was proposed to calculate shortest collision free path from source to destination by avoiding static as well as dynamic obstacles. Therefore, it is necessary to select appropriate optimization technique for optimization of paths. Such problems can be solved by metaheuristic methods. This research paper demonstrates the comparison and analysis of two Soft Computing Techniques i.e. Ant Colony Optimization (ACO) and Teaching Learning Based Optimization (TLBO) by simulating respective algorithms for finding shortest path of a Mobile Robot by Obstacle avoidance & Path re-planning and Path Tracking. Both of these techniques seem to be a promising technique with relatively competitive performances. The ACO has been more widely used in that and it gives good solution with smaller numbers of predetermined parameters in comparison with other algorithms.