An Objective Function to Evaluate Performance of Human-Robot Collaboration in Target Recognition Tasks

• Published in: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 39; no. 6; pp. 611 - 620
• Main Authors: Bechar, A., Meyer, J., Edan, Y.
• Format: Journal Article
• Language: English
• Published: New-York, NY IEEE 01.11.2009; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Collaboration; Computer science; control theory; systems; Control theory. Systems; Cost function; Exact sciences and technology; Human-robot interaction; Humans; Impedance; levels of automation; Numerical analysis; objective function; Robot sensing systems; Robotics; Signal detection; System performance; Target recognition; Uncertainty; unstructured environments; Human; Human operator; Man machine system; Objective analysis; Measurement sensor; levels of automation; Ground truth; Robotics; target recognition; Uncertain system; unstructured environments; Human-robot interaction; Object detection; Objective function; Reliability; Incomplete information; Signal detection
• ISSN: 1094-6977; 1558-2442
• DOI: 10.1109/TSMCC.2009.2020174

Robotic systems in unstructured environments must cope with unknown, unpredictable, and dynamic situations. Inherent uncertainty, and limited sensor accuracy and reliability impede target recognition performance. Introducing a human operator into the system can help improve performance and simplify the robotic system. In this paper, four basic levels of collaboration were defined for human-robot collaboration in target recognition tasks. An objective function that includes operational and time costs was developed to quantify performance and determine the best collaboration level. Signal detection theory was applied to evaluate system performance. The optimal collaboration level for different cases was determined by using numerical analyses of the objective function. The findings indicate that the best system performance, the optimal values of performance measures, and the best collaboration level depend on the task, the environment, human and robot parameters, and the system characteristics. For the tested cases, the manual level was never the best collaboration level for achieving the optimal solution. The autonomous level was the best collaboration level when robot sensitivity was higher than human sensitivity. In general, collaboration of human and robot in target recognition tasks will improve upon the optimal performance of a single human detector.