Hostname: page-component-77c89778f8-swr86 Total loading time: 0 Render date: 2024-07-18T10:47:53.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Performance analysis of a human–robot collaborative target recognition system

Published online by Cambridge University Press:  03 October 2011

Y. Oren ,
A. Bechar  and
Y. Edan
Y. Oren
Affiliation:
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
A. Bechar*
Affiliation:
Institute of Agricultural Engineering, The Volcani Center, A.R.O. Bet-Dagan, Israel
Y. Edan
Affiliation:
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel
*
*Corresponding author. E-mail: avital@volcani.agri.gov.il
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a comprehensive analysis of time and action operational costs on an objective function developed by Bechar et al. (A. Bechar, J. Meyer and Y. Edan, “An objective function to evaluate performance of human–robot collaboration in target recognition tasks,” IEEE Trans. Syst. Man Cybern. Part C39(6), 611–620 (2009)) for collaborative target recognition systems. Different task types, system reaction types, and environments were evaluated. Results reveal two types of task and system reactions – one focused on minimizing false alarms, and the second on detecting a target when one is presented. In addition, the analysis reveals a new property of the objective function based on a specific ratio between the weight differences that generalizes the model's objective function and facilitates its analysis. Results indicate that human decision time strongly influences system performance.

Keywords

Operational levelHuman–robot collaborationObjective functionPerformance analysisOperational costTarget recognition
Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 813 - 826
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Everett, S. and Dubey, R., “Human–machine cooperative telerobotics using uncertain sensor and model data,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (May 16–20, 1998) pp. 1615–1622.Google Scholar
2.Bechar, A. and Edan, Y., “Human–robot collaboration for improved target recognition of agricultural robots,” Ind. Robot 30 (5), 432–436 (2003).CrossRefGoogle Scholar
3.Cremer, F., Schutte, K., Schavemaker, J. G. M. and den Breejen, E., “A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection,” Inf. Fusion 2 (3), 187–208 (2001).CrossRefGoogle Scholar
4.Chang, Y. C., Song, G. S. and Hsu, S. K., “Automatic extraction of ridge and valley axes using the profile recognition and polygon-breaking algorithm,” Comput. Geosci. 24 (1), 83–93 (1998).CrossRefGoogle Scholar
5.Penin, L. F., Aracil, R., Ferre, M., Pinto, E., Hernando, M. and Barrientos, A., “Telerobotic System for Live Power Lines Maintenance: ROBTET,” In: Proceedings of the IEEE International Conference of Robotics and Automation, Leuven, Belgium (May 16–20, 1998) pp. 2210–2115.Google Scholar
6.Sun, Z., Bebis, G. and Miller, R., “Object recognition using feature subset selection,” Pattern Recognit. 37 (11), 2165–2176 (2004).CrossRefGoogle Scholar
7.Medicherla, H. and Sekmen, A., “Human–robot interaction via voice-controllable intelligent user interface,” Robotica 25, 521–527 (2007).CrossRefGoogle Scholar
8.Sheridan, T. B., Telerobotics, Automation, and Supervisory Control (MIT Press, Cambridge, MA, 1992).Google Scholar
9.Scholtz, J., “Theory and Evaluation of Human Robot Interaction,” Proceedings of the IEEE Hawaii International Conference on System Sciences, Hawaii (Jan. 6–9, 2003).Google Scholar
10.Howard, A. M., “Role Allocation in Human–Robot Interaction Schemes for Mission Scenario Execution,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Orlando, Florida (May 15–19, 2006) pp. 3588–3594.Google Scholar
11.Bechar, A., Edan, Y. and Meyer, J., “Optimal Collaboration in Human–Robot Target Recognition Systems,” In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Taipei, Taiwan (Oct. 8–11, 2006) pp. 4243–4248.Google Scholar
12.Bechar, A., Meyer, J. and Edan, Y., “An objective function to evaluate performance of human–robot collaboration in target recognition tasks,” IEEE Trans. Syst. Man Cybern. Part C 39 (6), 611–620 (2009).CrossRefGoogle Scholar
13.Ye, Y. M. and Tsotsos, J. K., “Sensor planning for 3D object search,” Comput. Vis. Image Underst. 73 (2), 145–168 (1999).CrossRefGoogle Scholar
14.Bicho, E., Mallet, P. and Schoner, G., “Target representation on an autonomous vehicle with low-level sensors,” Int. J. Robot. Res. 19 (5), 424–447 (2004).CrossRefGoogle Scholar
15.Zweig, M. H. and Campbell, G., “Receiver-operating characteristic (ROC) plots – a fundamental evaluation tool in clinical medicine,” Clin. Chem. 39 (4), 561–577 (1993).CrossRefGoogle ScholarPubMed
16.Griner, P. F., Mayewski, R. J., Mushlin, A. I. and Greenland, P., “Selection and interpretation of diagnostic tests and procedures. Principles and applications,” Ann. Intern. Med. 94 (4), 557–592 (1981).Google Scholar
17.Steinfeld, A., “Interface lessons for fully and semi-autonomous mobile robots,” Proc. IEEE Int. Conf. Robot. Autom. 3, 2752–2757 (2004).Google Scholar
18.Shin, D., Wysk, R. A. and Rothrock, L., “A formal control-theoretic model of a human-automation interactive manufacturing system control,” Int. J. Prod. Res. 44 (20), 4273–4295 (2006).CrossRefGoogle Scholar
19.Bechar, A., “Human–Robot Collaboration Methods for Target Recognition in Unstructured Environments,” Ph.D. Dissertation (Ben-Gurion University of the Negev, Beersheba, Israel, 2006).Google Scholar
20.Deb, K., Thiele, L., Laumanns, M. and Zitzler, E., “Scalable multi-objective optimization test problems,” In: Proceedings of the 2002 Congress on Evolutionary Computation 1, Honolulu, HI, USA (May 2002) pp. 825–830.Google Scholar
21.Ko, J. P. and Lee, J. M., “Tactile tele-operation of a mobile robot with a collision vector,” Robotica 24, 11–21 (2006).CrossRefGoogle Scholar
22.Swets, J. A., Dawes, R. M. and Monahan, J., “Better decisions through science,” Sci. Am. 283 (4), 82–87 (2000).CrossRefGoogle ScholarPubMed
23.Maltz, M. and Meyer, J., “Use of Warnings in an attentionally demanding detection task,” Hum. Factor 43 (2), 217–226 (2001).CrossRefGoogle Scholar
24.Maltz, M. and Shinar, D., “New alternative methods of analyzing human behavior in cued target acquisition,” Hum. Factors 45 (2), 281–295 (2003).CrossRefGoogle ScholarPubMed
25.Tkach, I., Bechar, A. and Edan, Y., “Switching between collaboration levels in a human–robot target recognition system,” IEEE Trans. Syst. Man Cybern. Part C (accepted).Google Scholar