Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-10T05:57:19.088Z Has data issue: false hasContentIssue false
  • English
  • Français

Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields

Published online by Cambridge University Press:  16 November 2017

C. Ton ,
Z. Kan  and
S. S. Mehta
C. Ton*
Affiliation:
Research and Engineering Education Facility, University of Florida, Shalimar, FL, USA
Z. Kan
Affiliation:
Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA, USA. E-mail: kanzhen0322@gmail.com
S. S. Mehta
Affiliation:
Department of Industrial and Systems Engineering, University of Florida, Shalimar, FL, USA. E-mail: siddhart@ufl.edu
*
*Corresponding author. E-mail: chau.t.ton@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

This paper considers applications where a human agent is navigating a semi-autonomous mobile robot in an environment with obstacles. The human input to the robot can be based on a desired navigation objective, which may not be known to the robot. Additionally, the semi-autonomous robot can be programmed to ensure obstacle avoidance as it navigates the environment. A shared control architecture can be used to appropriately fuse the human and the autonomy inputs to obtain a net control input that drives the robot. In this paper, an adaptive, near-continuous control allocation function is included in the shared controller, which continuously varies the control effort exerted by the human and the autonomy based on the position of the robot relative to obstacles. The developed control allocation function facilitates the human to freely navigate the robot when away from obstacles, and it causes the autonomy control input to progressively dominate as the robot approaches obstacles. A harmonic potential field-based non-linear sliding mode controller is developed to obtain the autonomy control input for obstacle avoidance. In addition, a robust feed-forward term is included in the autonomy control input to maintain stability in the presence of adverse human inputs, which can be critical in applications such as to prevent collision or roll-over of smart wheelchairs due to erroneous human inputs. Lyapunov-based stability analysis is presented to guarantee finite-time stability of the developed shared controller, i.e., the autonomy guarantees obstacle avoidance as the human navigates the robot. Experimental results are provided to validate the performance of the developed shared controller.

Keywords

Shared controlRobust controlObstacle avoidanceHarmonic potential fieldAdjustable autonomy
Type
Articles
Information
Robotica , Volume 36 , Issue 4 , April 2018 , pp. 463 - 483
Copyright
Copyright © Cambridge University Press 2017 

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. Sheridan, T. B., “Telerobotics,” Automatica 25 (4), 487–507 (1989).Google Scholar
2. Sheridan, T. B., Telerobotics, Automation, and Human Supervisory Control (MIT Press, Cambridge, MA, USA, 1992).Google Scholar
3. Ding, X. C., Powers, M., Egerstedt, M., Young, S. Yih and Balch, T., “Executive decision support,” IEEE Robot. Autom. Mag. 16 (2), 73–81 (2009).Google Scholar
4. Nudehi, S., Mukherjee, R. and Ghodoussi, M., “A shared-control approach to haptic interface design for minimally invasive telesurgical training,” IEEE Trans. Control Syst. Technol. 13 (4), 588–592 (2005).CrossRefGoogle Scholar
5. Kim, J., Ladjal, H., Folio, D., Ferreira, A. and Kim, J., “Evaluation of telerobotic shared control strategy for efficient single-cell manipulation,” IEEE Trans. Autom. Sci. Eng. 9 (2), 402–406 (2012).Google Scholar
6. Lee, S. and Lee, H. S., “Modeling, design, and evaluation of advanced teleoperator control systems with short time delay,” IEEE Trans. Robot. Autom. 9 (5), 607–623 (1993).Google Scholar
7. Kofman, J., Wu, X., Luu, T. and Verma, S., “Teleoperation of a robot manipulator using a vision-based human-robot interface,” IEEE Trans. Ind. Electron. 52 (5), 1206–1219 (2005).Google Scholar
8. Griffin, W., Provancher, W. and Cutkosky, M., “Feedback strategies for telemanipulation with shared control of object handling forces,” Presence 14 (6), 720–731 (2005).Google Scholar
9. Franchi, A., Secchi, C., Ryll, M., Bulthoff, H. and Giordano, P., “Shared control : Balancing autonomy and human assistance with a group of quadrotor UAVs,” IEEE Robot. Autom. Mag. 19 (3), 57–68 (2012).CrossRefGoogle Scholar
10. Aigner, P. and McCarragher, B., “Shared control framework applied to a robotic aid for the blind,” IEEE Control Syst. 19 (2), 40–46 (1999).Google Scholar
11. Aigner, P. and McCarragher, B. J., “Modeling and constraining human interactions in shared control utilizing a discrete event framework,” IEEE Trans. Man Cybern. Part A: Syst. Humans 30 (3), 369–379 (2000).Google Scholar
12. Cooper, R., Corfman, T., Fitzgerald, S., Boninger, M., Spaeth, D., Ammer, W. and Arva, J., “Performance assessment of a pushrim-activated power-assisted wheelchair control system,” IEEE Trans. Control Syst. Technol. 10 (1), 121–126 (2002).Google Scholar
13. Cipriani, C., Zaccone, F., Micera, S. and Carrozza, M., “On the shared control of an EMG-controlled prosthetic hand: Analysis of user-prosthesis interaction,” IEEE Trans. Robot. 24 (1), 170–184 (2008).Google Scholar
14. Yu, H., Spenko, M. and Dubowsky, S., “An adaptive shared control system for an intelligent mobility aid for the elderly,” Auton. Robots 15 (1), 53–66 (2003).CrossRefGoogle Scholar
15. Urdiales, C., Peula, J. M., Fdez-Carmona, M., Barrué, C., Pérez, E. J., Sánchez-Tato, I., Toro, J. C., Galluppi, F., Cortés, U., Annichiaricco, R., Caltagirone, C. and Sandoval, F., “A new multi-criteria optimization strategy for shared control in wheelchair assisted navigation,” Auton. Robots 30 (2), 179–197 (2010).Google Scholar
16. Wang, H. and Liu, X., “Adaptive shared control for a novel mobile assistive robot,” IEEE/ASME Trans. Mechatronics 19 (6), 1725–1736 (2014).Google Scholar
17. Kim, H., Biggs, J., Schloerb, D. W., Carmena, J., Lebedev, M., Nicolelis, M. and Srinivasan, M., “Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces,” IEEE Trans. Biomed. Eng. 53 (6), 1164–1173 (2006).CrossRefGoogle ScholarPubMed
18. Borenstein, J. and Koren, Y., “Teleautonomous guidance for mobile robots,” IEEE Trans. Syst. Man Cybern. 20 (6), 1437–1443 (1990).Google Scholar
19. Venkataraman, S. and Hayati, S., “Shared/traded control of telerobots under time delay,” Comput. Electr. Eng. 19 (6), 481–494 (1993).Google Scholar
20. Hirzinger, G., Brunner, B., Dietrich, J. and Heindl, J., “Sensor-based space robotics-rotex and its telerobotic features,” IEEE Trans. Robot. Autom. 9 (5), 649–663 (1993).Google Scholar
21. Fong, T. W., Thorpe, C. and Baur, C., “A Safeguarded Teleoperation Controller,” Proceedings of the IEEE International Conference on Advanced Robotics, Budapest, Hungary (2001).Google Scholar
22. Bruemmer, D., Few, D., Boring, R., Marble, J., Walton, M. and Nielsen, C., “Shared understanding for collaborative control,” IEEE Trans. Man Cybern. Part A: Syst. Humans 35 (4), 494–504 (2005).Google Scholar
23. Khademian, B. and Hashtrudi-Zaad, K., “Performance Issues in Collaborative Haptic Training,” Proceedings of the IEEE International Conference on Robotics and Automation (2007) pp. 3257–3262.Google Scholar
24. Khademian, B. and Hashtrudi-Zaad, K., “Shared control architectures for haptic training: Performance and coupled stability analysis,” Int. J. Robot. Res. 30 (13), 1627–1642 (2011).Google Scholar
25. Khademian, B., Apkarian, J. and Hashtrudi-Zaad, K., “Assessment of environmental effects on collaborative haptic guidance,” Presence 20 (3), 191–206 (2011).Google Scholar
26. Khademian, B. and Hashtrudi-Zaad, K., “Dual-user teleoperation systems: New multilateral shared control architecture and kinesthetic performance measures,” IEEE/ASME Trans. Mechatronics 17 (5), 895–906 (2012).CrossRefGoogle Scholar
27. Powell, D. and O'Malley, M., “The task-dependent efficacy of shared control haptic guidance paradigms,” IEEE Trans. Haptics 5 (3), 208–219 (2012).CrossRefGoogle ScholarPubMed
28. Song, K. T., Jiang, S. Y. and Lin, M. H.. “Interactive teleoperation of a mobile manipulator using a shared-control approach,” IEEE Trans. Human-Mach. Syst. 46 (6), 834–845 (2016).CrossRefGoogle Scholar
29. Hansson, A. and Servin, M., “Semi-autonomous shared control of large-scale manipulator arms,” Control Eng. Pract. 18 (9), 1069–1076 (2010).Google Scholar
30. Poncela, A., Urdiales, C., Perez, E. and Sandoval, F., “A new efficiency weighted strategy for continuous human/robot cooperation in navigation,” IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 39 (3), 486–500 (2009).Google Scholar
31. Ren, W. and Beard, R., “Satisficing Approach to Human-in-the-Loop Safeguarded Control,” Proceedings of the American Control Conference, Portland, OR, USA (2005) pp. 4985–4990.Google Scholar
32. Jiang, J. and Astolfi, A., “Shared-Control for the Kinematic Model of a Mobile Robot,” Proceedings of the IEEE 53rd Annual Conference on Decision and Control, Los Angeles, CA, USA (2014) pp. 62–67.Google Scholar
33. Jiang, J., Di Franco, P. and Astolfi, A.. “Shared control for the kinematic and dynamic models of a mobile robot,” IEEE Trans. Control Syst. Technol. 24 (6), 2112–2124 (2016).Google Scholar
34. Jiang, J. and Astolfi, A., “State and output-feedback shared-control for a class of linear constrained systems,” IEEE Trans. Autom. Control 61 (10), 3209–3214 (2016).Google Scholar
35. Erlien, S. M., Fujita, S. and Gerdes, J. C.. “Shared steering control using safe envelopes for obstacle avoidance and vehicle stability,” IEEE Trans. Intell. Transp. Syst. 17 (2), 441–451 (2016).Google Scholar
36. Storms, J. G. and Tilbury, D. M., “Blending of Human and Obstacle Avoidance Control for a High Speed Mobile Robot,” Proceedings of the American Control Conference, Portland, OR, USA (2014) pp. 3488–3493.Google Scholar
37. Jiang, S. Y., Lin, C. Y., Huang, K. T. and Song, K. T., “Shared control design of a walking-assistant robot,” IEEE Trans. Control Syst. Technol. 25 (6), 2143–2150 (2017).Google Scholar
38. Hayati, S. and Venkataraman, S., “Design and Implementation of a Robot Control System with Traded and Shared Control Capability,” Proceedings of the IEEE International Conference on Robotics and Automation, Scottsdale, AZ, USA (1989) pp. 1310–1315.Google Scholar
39. Anderson, S., Walker, J. and Iagnemma, K., “Experimental performance analysis of a homotopy-based shared autonomy framework,” IEEE Trans. Human-Mach. Syst. 44 (2), 190–199 (2014).Google Scholar
40. Guldner, J. and Utkin, V., “Sliding mode control for gradient tracking and robot navigation using artificial potential fields,” IEEE Trans. Robot. Autom. 11 (2), 247–254 (1995).Google Scholar
41. Wu, Z., Hu, G., Feng, L., Wu, J. and Liu, S., “Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling,” Assembly Autom. 36 (3), 318–332 (2016).Google Scholar
42. Wang, Z., Lam, J. and Burnham, K. J., “Stability analysis and observer design for neutral delay systems,” IEEE Trans. Autom. Control 47 (3), 478–483 (2002).Google Scholar
43. Hashimoto, H., Harashima, F., Utkin, V., Krasnova, S. and Kaliko, I., “Sliding Mode Control and Potential Fields in Obstacle Avoidance,” Proceedings of European Control Conference, Groningen, Netherlands (1993) pp. 859–862.Google Scholar