Abstract
We present information theoretic search strategies for single and multi-robot teams to localize the source of biochemical contaminants in turbulent flows. The robots synthesize the information provided by sporadic and intermittent sensor readings to optimize their exploration strategy. By leveraging the spatio-temporal sensing capabilities of a mobile sensing network, our strategies result in control actions that maximize the information gained by the team while minimizing the time spent localizing the biochemical source. By leveraging the team’s ability to obtain simultaneous measurements at different locations, we show how a multi-robot team is able to speed up the search process resulting in a collaborative information theoretic search strategy. We validate our proposed strategies in both simulations and experiments.
Highlights
We are interested in enabling autonomous mobile robot teams to collaboratively search and localize the source of a biochemical contaminant dispersed in turbulent media
We introduce the notion of the Value of Information (VoI) which provides us with a way to discriminate informative and useful data that should be used to update a robot’s estimate and data that should be ignored
We addressed the concept of the value of information and its role in evaluating what information to use to update the team’s estimate of the belief distribution describing the source location
Summary
We are interested in enabling autonomous mobile robot teams to collaboratively search and localize the source of a biochemical contaminant dispersed in turbulent media. Works in the first category focus on measuring the local concentration gradient of the plume along the robot’s trajectory to steer the robot towards the source with or without inferring the dynamics of the surrounding medium [5,6,7,8]. Since these strategies rely solely on the local gradient, they become unreliable when material concentrations do not vary in a smooth and continuous fashion.
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