Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-25T10:38:32.927Z Has data issue: false hasContentIssue false
  • English
  • Français

Development and analysis of an operator steering model for teleoperated mobile robots under constant and variable latencies

Published online by Cambridge University Press:  05 October 2016

Steve Vozar Open the ORCID record for Steve Vozar [Opens in a new window] ,
Justin Storms  and
D. M. Tilbury Open the ORCID record for D. M. Tilbury [Opens in a new window]
Steve Vozar*
Affiliation:
University of Michigan, Department of Computer Science and Engineering, Ann Arbor, MI 48109, USA E-mail: svozar@umich.edu
Justin Storms
Affiliation:
University of Michigan, Mechanical Engineering Department, Ann Arbor, MI 48109, USA E-mails: jgstorms@umich.edu, tilbury@umich.edu
D. M. Tilbury
Affiliation:
University of Michigan, Mechanical Engineering Department, Ann Arbor, MI 48109, USA E-mails: jgstorms@umich.edu, tilbury@umich.edu
*
*Corresponding author. E-mail: svozar@umich.edu
Get access Rights & Permissions [Opens in a new window]

Summary

Latency hinders a mobile robot teleoperator's ability to perform remote tasks. However, this effect is not well modeled. This paper develops a model for teleoperator steering behavior as a PD controller based on projected lateral displacement, which was tuned to reflect user performance determined by a 31-subject user study under constant and variable latency (having mean latencies between 0 and 750 ms). Additionally, we determined that operator performance under variable latency could be mapped to the expected performance of an equivalent constant latency. We then tested additional latency distributions in simulation and demonstrated equivalent steering performance among several different latency distributions.

Keywords

TeleroboticsUser modelsLatencyHuman–robot interactionWheeled robots
Type
Articles
Information
Robotica , Volume 36 , Issue 2 , February 2018 , pp. 167 - 186
Copyright
Copyright © Cambridge University Press 2016 

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. Luck, J. P., McDermott, P. L., Allender, L. and Russell, D. C., “An Investigation of Real World Control of Robotic Assets Under Communication Latency,” Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, HRI'06, New York, NY, USA, ACM (2006) pp. 202–209.Google Scholar
2. Davis, J., Smyth, C. and McDowell, K., “The effects of time lag on driving performance and a possible mitigation,” IEEE Trans. Robot. 26 (3), 590–593 (Jun. 2010).Google Scholar
3. Harriott, C. E. and Adams, J. A., “Modeling human performance for human-robot systems,” Rev. Human Factors Ergon. 9, 94–130 (Nov. 2013).Google Scholar
4. Yip, M. C., Tavakoli, M. and Howe, R. D., “Performance analysis of a haptic telemanipulation task under time delay,” Adv. Robot. 25 (5), 651–673 (2011).Google Scholar
5. Yang, T., Fu, Y. and Tavakoli, M., “Digital versus analog control of bilateral teleoperation systems: A task performance comparison,” Control Eng. Pract. 38, 46–56 (2015).Google Scholar
6. MacAdam, C. C., “Understanding and modeling the human driver,” Veh. Syst. Dyn. 40 (1–3), 101–134 (2003).CrossRefGoogle Scholar
7. Chen, J., Haas, E. and Barnes, M., “Human performance issues and user interface design for teleoperated robots,” IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 37 (6), 1231–1245 (2007).Google Scholar
8. Vozar, S., A Framework for Improving the Speed and Performance of Teleoperated Mobile Manipulators Ph.D. Thesis (Ann Arbor: University of Michigan, Aug. 2013).Google Scholar
9. Sheridan, T. B. and Ferrell, W. R., “Remote manipulative control with transmission delay,” IEEE Trans. Human Factors Electron. HFE-4 (1), 25–29 (Sep. 1963).Google Scholar
10. Slawiñski, E. and Mut, V., “Control scheme including prediction and augmented reality for teleoperation of mobile robots,” Robotica 28 (01), 11–22 (2010).Google Scholar
11. Xiong, Y., Li, S. and Xie, M., “Predictive display and interaction of telerobots based on augmented reality,” Robotica 24 (04), 447–453 (2006).Google Scholar
12. Sheik-Nainar, M. A., Kaber, D. B. and Chow, M.-Y., “Control gain adaptation in virtual reality mediated human–telerobot interaction,” Human Factors Ergon. Manuf. Service Ind. 15 (3), 259–274 (2005).Google Scholar
13. Goodrich, M. A., Olsen, D. R., Crandall, J. and Palmer, T. J., “Experiments in Adjustable Autonomy,” Proceedings of IJCAI Workshop on Autonomy, Delegation and Control: Interacting with Intelligent Agents (2001) pp. 1624–1629.Google Scholar
14. Marge, M., Powers, A., Brookshire, J., Jay, T., Jenkins, O. C. and Geyer, C., “Comparing heads-up, hands-free operation of ground robots to teleoperation,” Proceedings of Robotics: Science and Systems VII (2012) p. 193.Google Scholar
15. Wang, B., Li, Z. and Ding, N., “Speech Control of a Teleoperated Mobile Humanoid Robot,” Proceedings of 2011 IEEE International Conference on Automation and Logistics ICAL (2011) pp. 339–344.Google Scholar
16. Lane, J. Corde Carignan, C., Sullivan, B., Akin, D., Hunt, T. and Cohen, R., “Effects of Time Delay on Telerobotic Control of Neutral Buoyancy Vehicles,” Proceedings of the IEEE International Conference on Robotics and Automation, ICRA '02, vol. 3 (2002) pp. 2874–2879.Google Scholar
17. Lee, D., Martinez-Palafox, O. and Spong, M. W., “Bilateral Teleoperation of a Wheeled Mobile Robot Over Delayed Communication Network,” Proceedings of the IEEE Internation Robotics and Automation ICRA, (2006) pp. 3298–3303.Google Scholar
18. Hashemzadeh, F. and Tavakoli, M., “Position and force tracking in nonlinear teleoperation systems under varying delays,” Robotica 33 (04), 1003–1016 (2015).Google Scholar
19. Janabi-Sharifi, F. and Hassanzadeh, I., “Experimental analysis of mobile-robot teleoperation via shared impedance control,” IEEE Trans. Syst. Man Cybern. Part B: Cybern. 41 (2), 591–606 (2011).Google Scholar
20. Sanders, D., “Analysis of the effects of time delays on the teleoperation of a mobile robot in various modes of operation,” Ind. Robot: Int. J. 36 (6), 570–584 (2009).Google Scholar
21. McCracken, H., I Drove Ford's Golf Cart in Atlanta (Note: I Was in Silicon Valley at the Time), Fast Company (Jan. 2015).Google Scholar
22. Fitts, P. M., “The information capacity of the human motor system in controlling the amplitude of movement,” J. Exp. Psychol. 47 (6), 381 (1954).Google Scholar
23. Accot, J. and Zhai, S., “Beyond Fitts' Law: Models for Trajectory-Based HCI Tasks,” Proceedings of the ACM SIGCHI Conference on Human Factors in Computing SystemsCHI'97, New York, NY, USA, ACM (1997) pp. 295–302.Google Scholar
24. Zhai, S., Accot, J. and Woltjer, R., “Human action laws in electronic virtual worlds: An empirical study of path steering performance in VR,” Presence: Teleoperators Virtual Environ. 13 (2), 113–127 (Apr. 2004).Google Scholar
25. Pavlovych, A. and Stuerzlinger, W., “Target Following Performance in the Presence of Latency, Jitter and Signal Dropouts,” Proceedings of Graphics InterfaceGI'11, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada, Canadian Human-Computer Communications Society (2011), pp. 33–40.Google Scholar
26. Kaber, D., Li, Y., Clamann, M. and Lee, Y.-S., “Investigating human performance in a virtual reality haptic simulator as influenced by fidelity and system latency,” IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 42 (6), 1562–1566 (2012).Google Scholar
27. MacKenzie, I. S. and Ware, C., “Lag as a Determinant of Human Performance in Interactive Systems,” Proceedings of the INTERACT'93 and CHI'93 Conference on Human Factors in Computing System, New York, NY, USA, ACM (1993) pp. 488–493.Google Scholar
28. Tipsuwan, Y. and Chow, M.-Y., “Control methodologies in networked control systems,” Control Eng. Pract. 11 (10), 1099–1111 (2003).Google Scholar
29. Xie, X., Yin, S., Gao, H. and Kaynak, O., “Asymptotic stability and stabilisation of uncertain delta operator systems with time-varying delays,” Control Theory Appl. IET 7 (8), 1071–1078 (2013).Google Scholar
30. McRuer, D. T., “Human pilot dynamics in compensatory systems,” Technical report, DTIC Document (1965).Google Scholar
31. MacAdam, C. C., “An optimal preview control for linear systems,” J. Dyn. Syst. Meas. Control 102 (3), 188–190 (1980).Google Scholar
32. Van De Vegte, J. M., Milgram, P. and Kwong, R. H., “Teleoperator control models: Effects of time delay and imperfect system knowledge,” IEEE Trans. Syst. Man Cybern. 20 (6), 1258–1272 (1990).Google Scholar
33. Delice, I. and Ertugrul, S., “Intelligent Modeling of Human Driver: A Survey,” Proceedings of the 2007 IEEE Intelligent Vehicles Symposium (2007) pp. 648–651.Google Scholar
34. Palma, L. Brito, Coito, F. Vieira and Gil, P. Sousa, “Low Order Models for Human Controller–Mouse Interface,” Proceedings of the 2012 IEEE 16th International Conference on Intelligent Engineering Systems INES (2012) pp. 515–520.Google Scholar
35. Vozar, S. and Tilbury, D. M., “Driver Modeling for Teleoperation with Time Delay,” Proceedings of the 19th IFAC World Congress (2014) pp. 3551–3556.Google Scholar
36. Ritter, F. E., Kukreja, U. and Amant, R. S., “Including a model of visual processing with a cognitive architecture to model a simple teleoperation task,” J. Cogn. Eng. Decis. Making 1 (2), 121–147 (Jun. 2007).Google Scholar
37. ACT-r, http://act-r.psy.cmu.edu/ (Aug. 2014).Google Scholar
38. APRIL Robotics Laboratory, APRIL Laboratory: Autonomy * Perception * Robotics * Interfaces * Learning, http://april.eecs.umich.edu (Mar. 2012).Google Scholar
39. Huang, A., Olson, E. and Moore, D., “LCM: Lightweight Communications and Marshalling,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems IROS (Oct. 2010).Google Scholar
40. Ögren, P., Svenmarck, P., Lif, P., Norberg, M. and Söderbäck, N. E., “Design and Implementation of a New Teleoperation Control Mode for Differential Drive UGVs,” Auton. Robots (Nov. 2013), pp. 1–9.Google Scholar
41. iRobot Corporation, iRobot 510 PackBot – specifications (2012). Accessed online 2014.Google Scholar
42. Hollands, J. G. and Lamb, M., “Viewpoint tethering for remotely operated vehicles effects on complex terrain navigation and spatial awareness,” Human Factors: J. Human Factors Ergon. Soc. 53 (2), 154–167 (Apr. 2011).Google Scholar
43. Anand, D., Bhatia, M., Moyne, J., Shahid, W. and Tilbury, D., “Wireless test results booklet,” Technical report, University of Michigan ERC/RMS (2010).Google Scholar
44. E54 Committee, “Test method for evaluating emergency response robot capabilities: Mobility: Maneuvering tasks: Sustained speed,” Technical report, ASTM International (2011).Google Scholar
45. Son, H. I., Chuang, L., Franchi, A., Kim, J., Lee, D., Lee, S.-W., Bulthoff, H. and Giordano, P., “Measuring an Operator's Maneuverability Performance in the Haptic Teleoperation of Multiple Robots,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS) (2011) pp. 3039–3046.Google Scholar
46. Grabe, V., Pretto, P., Giordano, P. R. and Bülthoff, H. H., “Influence of Display Type on Drivers Performance in a Motion-Based Driving Simulator,” Proceedings of the Driving Simulation Conference (2010).Google Scholar
47. AUVSI, “Unmanned ground vehicles: Core capabilities & market background,” Technical report, The Association for Unmanned Vehicle Systems International (Aug. 2013).Google Scholar
48. Frigge, M., Hoaglin, D. C. and Iglewicz, B., “Some implementations of the boxplot,” Am. Statistician 43 (1), 50–54 (Feb. 1989).Google Scholar
49. Rouse, R. III,, “What's your perspective? SIGGRAPH Comput. Graph. 33 (3), 9–12 (Aug. 1999).Google Scholar
50. Pazuchanics, S. L., “The Effects of Camera Perspective and Field of View on Performance in Teleoperated Navigation,” Proc. Human Factors Ergon. Soc. Annu. Meet. 50 (16):1528–1532 (Oct. 2006).Google Scholar
51. Ulsoy, A. G., Peng, H. and Çakmakci, M., Automotive Control Systems (New York, NY, Cambridge University Press, 2012).Google Scholar
52. Nise, N. S., Control Systems Engineering, 4th ed. (Hoboken, NJ, John Wiley & Sons, 2004).Google Scholar
53. Ungoren, A. and Peng, H., “An adaptive lateral preview driver model,” Veh. Syst. Dyn. 43 (4), 245–259 (2005).CrossRefGoogle Scholar
54. Toffin, D., Reymond, G., Kemeny, A. and Droulez, J., “Role of steering wheel feedback on driver performance: Driving simulator and modeling analysis,” Veh. Syst. Dyn. 45 (4), 375–388 (2007).Google Scholar
55. Jagacinski, R. J., “A qualitative look at feedback control theory as a style of describing behavior,” Human Factors: J. Human Factors Ergon. Soc. 19 (4), 331–347 (1977).Google Scholar