Name
Title | ||
|---|---|---|
An objective function to evaluate performance of human-robot collaboration in target recognition tasks |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSMCC.2009.2020174 | IEEE Transactions on Systems, Man, and Cybernetics, Part C |
Keywords | Field | DocType |
optimal collaboration level,basic level,manual level,best system performance,autonomous level,human-robot collaboration,target recognition task,robotic system,best collaboration level,objective function,human robot interaction,cost function,numerical analysis,signal detection theory,sensitivity analysis,image sensors,impedance,uncertainty,signal detection,object recognition,collaboration,system performance | Object detection,Mathematical optimization,Detection theory,Computer science,Ground truth,Artificial intelligence,Robot,Machine learning,Complete information,Robotics,Human–robot interaction,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
39 | 6 | 1094-6977 |
Citations | PageRank | References |
6 | 0.51 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Avital Bechar | 1 | 33 | 4.82 |
| Joachim Meyer | 2 | 376 | 41.28 |
| Yael Edan | 3 | 514 | 53.29 |