Analyses of Parametric Relation in Ant Colony Approach for Robot Path Planning Problem

One of the most challenging areas of robotics research is the mobile robot vehicle in a complicated obstacle environment. Recently, algorithms for autonomous mobile vehicles have largely been influenced by nature-based algorithms. The swarm behavior of many different organisms, such as ants, fireflies, honey bees, and foxes, is simulated by several algorithms in mobile robot research, and the results are highly interesting and inspiring. In this paper, we took the inspired algorithm as an ant colony which is used to solve the path planning problems for any mobile vehicle/robots. While studying and using the ant colony approaches to solve the problem, we found that the parameter selections are one of the important expects to make the results of any application worse or better. Therefore, we decided to perform the study on the parametric relation in an ant colony approach. The importance of utilization of the Ant Colony optimization (ACO) approach is to control the parameters. In this paper, we analysed/investigated the relationship between the different control parameters of the ACO algorithm and their performance in the resolution of path planning problem. Simulation is performed with different parameter values and by the result analysis, a selection of parameter is presented.