Optimal HumanMachine Teaming for a Sequential Inspection Operation
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© 2013 IEEE. A novel mixed initiative optimal control system for intelligence, surveillance and reconnaissance (ISR) operations which entails human-machine teaming has been developed. The scenario entails a camera-equipped unmanned air vehicle sequentially overflying geolocated objects of interest, which need to be classified as either a true or false target by a human operator. The vehicle is allowed a prespecified number of revisits, such that an object can be looked at, a second time, under better viewing conditions. The overarching goal is to correctly classify the objects and minimize the false alarm (FA) and missed detection (MD) rates. We design a stochastic controller that computes if and when a revisit is necessary and also the optimal revisit state, i.e., viewing altitude and aspect angle. The concept of operation is such that the critical task of detection/pattern recognition is relegated to the human operator, whereas optimal decision making is entrusted to the machine. The stochastic dynamic programming-based decision algorithm is, however, informed about the performance of the human operator via an empirical human perception model. The model is experimentally obtained in the form of state-dependent confusion matrices. The optimal closed-loop ISR system is shown to experimentally achieve a FA rate of 5% and MD rate of 12%, which are significantly lower than the open-loop operator-only performance metrics. The performance improvements that were observed are relevant to a particular operator, and thus, the study suggests that the same improvements could conceivably be achieved with other test subjects.
author list (cited authors)
Kalyanam, K., Pachter, M., Patzek, M., Rothwell, C., & Darbha, S.