Generalized and modified ant algorithm for solving robot path planning problem

The task of planning trajectories for a mobile robot has received considerable attention in the research literature. The problem involves computing a collision-free path between a start point and a target point in environment of known obstacles. In this paper, we introduced the generalized and modified ant algorithm for solving robot path planning, by the term generalized we mean ant can select either one of the 16, 24 or 32 neighbor points for its next movement as contrast to simple one in which only one of the eight neighborhoods can be selected by the ant. As per the general theory of the graph search algorithms, the increase in the number of neighborhood points make the solutions more optimal in terms of path length, but put a limitation on the execution time which increases drastically with an increase in the number of neighborhood points. Our simulation results show that there is a considerable decrease in the path length with the increase in the level of generalization, the time of execution however increases but the algorithm performance can be improved by modified ant algorithm in terms of execution time.