Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T09:51:24.615Z Has data issue: false hasContentIssue false

Dynamic IBVS of a rotary wing UAV using line features

Published online by Cambridge University Press:  09 December 2014

Hui Xie
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4
Alan F. Lynch*
Affiliation:
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4
Martin Jagersand
Affiliation:
Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8
*
*Corresponding author. Email: alan.lynch@ualberta.ca

Summary

In this paper we propose a dynamic image-based visual servoing (IBVS) control for a rotary wing unmanned aerial vehicle (UAV) which directly accounts for the vehicle's underactuated dynamic model. The motion control objective is to follow parallel lines and is motivated by power line inspection tasks where the UAV's relative position and orientation to the lines are controlled. The design is based on a virtual camera whose motion follows the onboard physical camera but which is constrained to point downwards independent of the vehicle's roll and pitch angles. A set of image features is proposed for the lines projected into the virtual camera frame. These features are chosen to simplify the interaction matrix which in turn leads to a simpler IBVS control design which is globally asymptotically stable. The proposed scheme is adaptive and therefore does not require depth estimation. Simulation results are presented to illustrate the performance of the proposed control and its robustness to calibration parameter error.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

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. Kendoul, F., “Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems,” J. Field Robot. 29 (2), 315378 (2012).Google Scholar
2. R&D Program Office, System Planning and Asset Management Div., BCTC, Transmission Technology Roadmap: Pathways to BC's Future Grid (2008).Google Scholar
3. Whitworth, C., Duller, A., Jones, D. and Earp, G., “Aerial video inspection of overhead power lines,” Power Eng. J. 15, 2532 (Feb. 2001).Google Scholar
4. Azinheira, J. R. and Rives, P., “Image-based visual servoing for vanishing features and ground lines tracking: Application to a UAV automatic landing,” Int. J. Optomechatronics 2 (3), 275295 (2008).CrossRefGoogle Scholar
5. Chaumette, F. and Hutchinson, S., “Visual servo control part I: Basic approaches,” IEEE Robot. Autom. Mag. 13 (4), 8290 (2006).CrossRefGoogle Scholar
6. Chaumette, F. and Hutchinson, S., “Visual servo control part II: Advanced approaches,” IEEE Robot. Autom. Mag. 14 (1), 109118 (2007).Google Scholar
7. Rondon, E., Garcia-Carrillo, L.-R. and Fantoni, I., “Vision-Based Altitude, Position and Speed Regulation of a Quadrotor Rotorcraft,” Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, (Taipei, Taiwan) (Oct. 2010) pp. 628–633.Google Scholar
8. Carrillo, L., Flores Colunga, G., Sanahuja, G. and Lozano, R., “Quad rotorcraft switching control: An application for the task of path following,” IEEE Trans. Control Syst. Technol. 22 (4), 12551267 (2014).Google Scholar
9. Espiau, B., “Effect of camera calibration errors on visual servoing in robotics,” In: Experimental Robotics III (Yoshikawa, T. and Miyazaki, F., eds.) Lecture Notes in Control and Information Sciences, vol. 200 (Springer, Berlin, 1994) pp. 182192.CrossRefGoogle Scholar
10. Corke, P. and Good, M., “Dynamic Effects in High-Performance Visual Servoing,” Proceedings of the 1992 IEEE International Conference on Robotics and Automation, Nice, France (May 1992) pp. 1838–1843.Google Scholar
11. Komuro, T., Iwashita, A. and Ishikawa, M., “A QVGA-size pixel-parallel image processor for 1,000-FPS vision,” IEEE Micro 29 (6), 5867 (2009).CrossRefGoogle Scholar
12. Hamel, T. and Mahony, R., “Visual servoing of an under-actuated dynamic rigid-body system: an image-based approach,” IEEE Trans. Robot. Autom. 18 (2), 187198 (2002).CrossRefGoogle Scholar
13. Bourquardez, O., Mahony, R., Guenard, N., Chaumette, F., Hamel, T. and Eck, L., “Image-based visual servo control of the translation kinematics of a quadrotor aerial vehicle,” IEEE Trans. Robot. 25 (3), 743749 (2009).CrossRefGoogle Scholar
14. Mahony, R. and Hamel, T., “Image-based visual servo control of aerial robotic systems using linear image features,” IEEE Trans. Robot. 21 (2), 227239 (2005).CrossRefGoogle Scholar
15. Metni, N., Hamel, T. and Derkx, F., “Visual Tracking Control of Aerial Robotic Systems with Adaptive Depth Estimation,” Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, Seville, Spain (Dec. 2005) pp. 6078–6084.Google Scholar
16. de Plinval, H., Morin, P., Mouyon, P. and Hamel, T., “Visual Servoing for Underactuated VTOL UAVs: A Linear, Homography-Based Approach,” Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China (May 2011) pp. 3004–3010.CrossRefGoogle Scholar
17. de Plinval, H., Morin, P., Mouyon, P. and Hamel, T., “Visual servoing for underactuated VTOL UAVs: A linear, homography-based framework,” Int. J. Robust Nonlinear 24 (16), 22852308 (2014).Google Scholar
18. Ozawa, R. and Chaumette, F., “Dynamic Visual Servoing with Image Moments for a Quadrotor using a Virtual Spring Approach,” Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China (May 2011) pp. 5670–5676.Google Scholar
19. Ozawa, R. and Chaumette, F., “Dynamic visual servoing with image moments for an unmanned aerial vehicle using a virtual spring approach,” Adv. Robotics 27 (9), 683696 (2013).Google Scholar
20. Jabbari, H., Oriolo, G. and Bolandi, H., “An adaptive scheme for image-based visual servoing of an underactuated UAV,” Int. J. Robot. Autom. 29 (1), 92104 (2014).Google Scholar
21. Espiau, B., Chaumette, F. and Rives, P., “A new approach to visual servoing in robotics,” IEEE Trans. Robot. Autom. 8 (3), 313326 (1992).CrossRefGoogle Scholar
22. Godbolt, B., Experimental Nonlinear Control of a Helicopter Unmanned Aerial Vehicle (UAV) Ph.D. Thesis (Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, 2013).Google Scholar
23. De Luca, A., Oriolo, G. and Robuffo Giordano, P., “Feature depth observation for image-based visual servoing: Theory and experiments,” Int. J. Robot. Res. 27 (10), 10931116 (2008).Google Scholar
24. Tahri, O. and Chaumette, F., “Point-based and region-based image moments for visual servoing of planar objects,” IEEE Trans. Robot. 21 (6), 11161127 (2005).Google Scholar
25. Guenard, N., Hamel, T. and Mahony, R., “A practical visual servo control for an unmanned aerial vehicle,” IEEE Trans. Robot. 24 (2), 331340 (2008).CrossRefGoogle Scholar