Detecting regime changes with a mobile robot using multiple models

We present an approach to the detection of global environmental regime changes by a mobile robot performing a task. The approach is based on the use of augmented Markov models (AMMs), a variation of semi-Markov process. We have developed an algorithm that constructs AMMs on-line and in real-time with little computational and space overhead AMMs are a general tool for capturing the interaction dynamics between a robot and its environment using the history of behavior executed by the robot. We have demonstrated AMMs to be effective in applications such as fault detected, dynamic leader selection, and reward maximization in non-stationary environments. In this paper, we extend AMMs to regime detection, using multiple models to monitor events at different time scales and provide statistics to detect regime changes at those time scales. This approach has been successfully implemented using a physical mobile robot performing a land mine collection task. In the context of this task, we present experimental results, first validating our approach, then demonstrating a more complex proportion-maintaining scenario of the land mine collection task. Finally, we present results using an alternative reward maximization decision criterion in the same task.

Detecting regime changes with a mobile robot using multiple models Conference Paper