Policies for risk-aware sensor data collection by mobile agents.

We consider a scenario where mobile agents are required to collect measurements from a geographically dispersed set of sensors and return them to a base. An agent faces a risk of destruction while traversing the environment to reach the sensors, and gets a reward for gathering a sensor measurement only if it successfully returns to base. We first consider the single agent scenario and characterize several properties of the optimal policy for the agent based on a quantity that we term the "reward-to-risk" ratio of the tasks. We provide the optimal policy when the risk of destruction is sufficiently high, and then extend our analysis to multiple (heterogeneous) agents, with potentially different costs and survival probabilities. In particular, when the risk of destruction is sufficiently high, we show that the multi-agent task allocation problem has a submodular structure, and accordingly provide a greedy algorithm that is guaranteed to return an allocation that is within a 1 2 factor of optimal. [ABSTRACT FROM AUTHOR]