An Approach Based on Clustering Method for Object Finding Mobile Robots Using ACO

In this paper, we propose Clustering method and Ant Colony Optimization (ACO) for mobile robot. This paper describes the analysis and design of a new class of mobile robots. These small robots are intended to be simple and inexpensive, and will all be physically identical, thus constituting a homogeneous team of robots. They derive their usefulness from their group actions, performing physical tasks and making cooperative decisions as a Coordinated Team. To improve the performance of clustering, the method based on heuristic concept is used to obtain global search. The main advantage of clustering algorithm lies in the fact that no additional information, such as an initial partitioning of the data or the number of clusters, is needed. Since the proposed method is very efficient, thus it can perform object finding using clustering very quickly. In the process of doing so, we first use ACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles.