Robotic sensor data analysis using stream data mining techniques
Many robotic applications deploy multiple robots and it is possible that more than one of those robots are operating in the same environment. Such situations demand grouping together of similar environments in real-time to perform actions in a coordinated way. The main challenge when robots sent huge amount of data is to process the data stream without storing them. In this work, an experimental setup is created to gather data from simulated robotic environments. The data collected are treated as continuously arriving time series data and they are com-pressed using summary data structures suitable for clustering. The robotic environments are clustered using techniques based on simple single pass K-means and StreamKM++ algorithms. The methods used to adapt these two algorithms for robotics data streams are discussed. The suitability of these techniques for robotic applications is analyzed and performances of the algorithms are compared.
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