This paper addresses the problem of deploying a network of robots to gather information in an environment, where the environment is hazardous to the robots. This may mean that there are adversarial agents in the environment trying to disable the robots, or that some regions of the environment tend to make the robots fail, for example due to radiation, fire, adverse weather, or caustic chemicals. A probabilistic model of the environment is formulated, under which recursive Bayesian filters are used to estimate the environment events and hazards online. The robots must control their positions both to avoid sensor failures and to provide useful sensor information by following the analytical gradient of mutual information computed using these online estimates. Mutual information is shown to combine the competing incentives of avoiding failure and collecting informative measurements under a common objective. Simulations demonstrate the performance of the algorithm.
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