Experimentation and analysis of time series data for rescue robotics

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Abstract:

In today’s world, rescue robots are used in various life threatening situations where human help or support is not possible. These robots transfer real time data about the environment continuously. Research is focussed on techniques to analyse real time data to enable Decision Support Systems (DSS) to take timely actions to save lives. This paper discusses preliminary experiments that have been carried out to simulate a set of simple robotic environments. A robot attached with four sensors is used to collect information about the environments as the robot moves in a straight line path. Time series data collected from these experiments are clustered using data mining techniques. Experimental results show recall and precision between 73% to 98%.