Clustering of robotic environment using image data stream

Mobile robots are being used in various applications like space shuttles, intelligent home security, military applications or other service oriented applications where human intervention is limited. A robot has to understand its environment by analyzing the data to take the appropriate actions in the given environment. Mostly the data collected from the sensors on the robots are huge and continuous, making it impossible to store the entire data in main memory and hence allowing only single scan of data. Traditional clustering algorithms like k-means cannot be used in such environment as they require multiple scan of data. This paper presents an experimental study on the implementation of Stream KM++, a data stream clustering algorithm that effectively cluster these time series robotic image data within the memory restrictions under various conditions. Promising results are obtained from the various experiments carried out.