Active Chemical Sensing With Partially Observable Markov Decision Processes

We present an active-perception strategy to optimize the temperature program of metal-oxide sensors in real time, as the sensor reacts with its environment. We model the problem as a partially observable Markov decision process (POMDP), where actions correspond to measurements at particular temperatures, and the agent is to find a temperature sequence that minimizes the Bayes risk. We validate the method on a binary classification problem with a simulated sensor. Our results show that the method provides a balance between classification rate and sensing costs.