Towards Probabilistic Operator-Multiple Robot Decision Models

Abstract

Coupled operator-multiple vehicle systems are modelled in a unified framework using probabilistic graphs to yield a methodology for analyzing semi-autonomous systems. The framework uses conditional probabilistic dependencies between all elements, leading to a Bayesian network (BN) with probabilistic evaluation capability. Vehicle attitude/navigation states and target/classification states can be evaluated using nonlinear estimators such as the EKF, multiple model filter, information filter, or other approaches. Discrete operator decisions are being modeled as Bayesian network blocks, with conditional dependencies on the vehicle and tracking estimators. Initial decision models use combinations of softmax and discrete probability distributions.