Optimal Collaboration in Human-Robot Target Recognition Systems

This paper presents a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. Four human-robot collaboration levels for target recognition tasks were defined, tested and evaluated. The collaboration levels were adjusted to an extensive range of automation, from manual to fully autonomous. An objective function for target recognition in human-robot systems was developed to allow computation of the expected value of system performance given the human, robot, environmental and task parameters. The objective function includes operational and time costs that are both important in evaluation and optimization of system performance. It quantifies the multitude of influencing parameters through a weighted sum of performance measures, and enables to predict system performance and the desirable level of collaborations and to help design optimal systems for specific tasks. Numerical analysis of the developed objective function combined with signal detection theory was applied for the defined collaboration levels. Based on the numerical analysis, the human and robot optimal values were determined for different task and environment parameters. Results indicate that collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector. When robot sensitivity is higher than human sensitivity, and, the overall system sensitivity never decreases beneath the robot sensitivity the best collaboration level is autonomous robot operation.