Canopy Clustering Based Multi Robot Area Exploration

Various challenges are faced while covering a given area by a team of robots. One of the challenges is to efficiently cover the given area while reducing the repeated coverage. A partitioning method can be used so that each robot will explore its allocated sub area individually, thus avoiding collision and repeated coverage. Partitioning the environment presents the concept of workload sharing, which shows the profound effect in reducing the overall exploration time. In previous studies K Means algorithm has been used to partition the environment. K Means is based on centroid calculation method in which centroids are updated at every iteration until convergence. For calculation of centroid taken, it considers all the points in the environment thus resulting increasing clustering time.