Allocating Limited Sensing Resources to Accurately Map Dynamic Environments

Abstract

This work addresses the problem of learning a model of a dynamic environment using many independent Hidden Markov Models (HMMs) with a limited number of observations available per iteration. Many techniques exist to model dynamic environments, but do not consider how to deploy robots to build this model. Additionally, there are many techniques for exploring environments that do not consider how to prioritize regions when resources, in terms of robots to deploy and deployment durations, are limited. Here, we consider an environment model consisting of a series of HMMs that evolve over time independently and can be directly observed. At each iteration, we must determine which HMMs to observe in order to maximize the gain in model accuracy. We present a utility measure that balances a Pearson's χ 2 goodness-of-fit of the dynamics model with Mutual Information (MI) to ensure that observations are allocated to maximize the convergence rate of all HMMs, resulting in a faster convergence to higher steady-state model confidence and accuracy than either χ 2 or MI alone.