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Vertical Obstacle Avoidance and Navigation of Autonomous Underwater Vehicles with H∞ Controller and the Artificial Potential Field Method

Published online by Cambridge University Press:  20 August 2018

Shun-Min Wang*
Affiliation:
(Department of Systems & Naval Mechatronic Engineering, National Cheng-Kung University, Tainan, 70101, Taiwan)
Ming-Chung Fang
Affiliation:
(Department of Systems & Naval Mechatronic Engineering, National Cheng-Kung University, Tainan, 70101, Taiwan)
Cheng-Neng Hwang
Affiliation:
(Department of Systems & Naval Mechatronic Engineering, National Cheng-Kung University, Tainan, 70101, Taiwan)

Abstract

An H∞ controller combined with an Artificial Potential Field Method (APFM) was applied to seabed navigation for Autonomous Underwater Vehicles (AUVs), aimed particularly at obstacle avoidance and bottom-following operations in the vertical plane. Depth control and altitude control prevented the AUV from colliding with the sea bottom or with obstacles and prevented the AUV from diving beyond its maximum depth limit when bottom following. Simulation and laboratory trials with various seabed contours indicated that with the H∞ controller, the AUV was able to safely reach appointed destinations without collisions. Tests also showed that the H∞ controller was robust and suppressed interference, hence ensuring the precision of its navigation control. The proposed H∞ controller combined with the APFM has thus been proved to be both feasible and effective.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Antonelli, G., Chiaverini, S., Finotello, R., and Schiavon, R. (2001). Real-time path planning and obstacle avoidance for RAIS: an autonomous underwater vehicle. IEEE Journal of Oceanic Engineering, 26(2), 216227.Google Scholar
Cheng, C.L., Zhu, D.Q., Sun, B., Chu, Z.Z., Nie, J.D. and Zhang, S. (2015). Path planning for autonomous underwater vehicle based on artificial potential field and velocity synthesis. IEEE 28th Canadian Conference on Electrical and Computer Engineering, 717721.Google Scholar
Creuze, V. and Jouvencel, B. (2002). Avoidance of underwater cliffs for autonomous underwater vehicles. Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, 793798.Google Scholar
Ding, F.G., Jiao, P., Bian, X.G. and Wang, H.J. (2005). AUV local path planning based on virtual potential field. Proceedings of the IEEE International Conference on Mechatronics & Automation, 4, 17111716.Google Scholar
Doyle, J.C., Glover, K., Khargonekar, P.P. and Francis, B.A. (1989). State-space solutions to standard H2 and H∞ control problems. IEEE Transactions on Automatic Control, 34(8), 831847.Google Scholar
Fang, M.C., Chang, P.E. and Luo, J.H. (2006). Wave effects on ascending and descending motions of the autonomous underwater vehicle. Ocean Engineering, 33, 19721999.Google Scholar
Fang, M.C., Hou, C.S. and Luo, J.H. (2007). On the motions of the underwater remotely operated vehicle with the umbilical cable effect. Ocean Engineering, 34, 12751289.Google Scholar
Fang, M.C., Wang, S.M., Wu, M.C. and Lin, Y.H. (2015). Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles. Ocean Engineering, 93, 1124.Google Scholar
Feng, Z. and Allen, R. (2002). H∞ autopilot design for an autonomous underwater vehicle. Proceedings of the 2002 International Conference on Control Applications, 350354.Google Scholar
Gao, J., Xu, D., Zhao, N. and Yan, W. (2008). A potential field method for bottom navigation of autonomous underwater vehicles. Proceedings of the 7th World Congress on Intelligent Control and Automation, 74667470.Google Scholar
Gao, Y., Wei, Z.Q., Gong, F.X., Yin, B. and Ji, X.P. (2013). Dynamic path planning for underwater vehicles based on modified artificial potential field method. Fourth International Conference on Digital Manufacturing & Automation, 518521.Google Scholar
Ge, S.S. and Cui, Y.J. (2000). New potential functions for mobile robot path planning. IEEE Transactions on Robotics and Automation, 16(5), 615620.Google Scholar
Ge, S.S., and Cui, Y.J. (2002). Dynamic motion planning for mobile robots using potential field method. Autonomous Robots, 13, 207222.Google Scholar
Hanumant, S. (1995). Sonar mapping with the Autonomous Benthic Explorer (ABE). Proceedings of the 9th International Symposium on Unmanned Untethered Submersible Technology, 367375.Google Scholar
Hwang, C.N. (1993). Formulation of H2 and H∞ optimal control problems – a variational approach. Journal of the Chinese Institute of Engineers, 16(6), 853866.Google Scholar
Hwang, C.N. (2002). The integrated design of fuzzy collision-avoidance and H∞ -autopilots on ships. The Journal of Navigation, 55, 117136.Google Scholar
Kaminer, I., Pascoal, A.M. Silvestre, C.J. and Khargonekar, P.P. (1991). Control of an underwater vehicle using H-infinity synthesis. Proceedings of the 30th IEEE Conference on Decision and Control, 23502355.Google Scholar
Kanakakis, V., Valavanis, K.P. and Tsourveloudis, N.C. (2004). Fuzzy-logic based navigation of underwater vehicles. Journal of Intelligent and Robotic Systems, 40, 4588.Google Scholar
Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5(1), 9098.Google Scholar
Khosla, P. and Volpe, R. (1988). Superquadratic artificial potentials for obstacle avoidance and approach. Proceedings of the IEEE Conference on Robotics and Automation, 17781784.Google Scholar
Koren, Y. and Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. IEEE International Conference on Robotics and Automation, 2, 13981404.Google Scholar
Logan, C.L. (1994). A comparison between H_infinity/Mu_synthesis control and sliding mode control for robust control of a small autonomous underwater vehicle. Proceedings of the 1994 Symposium on Autonomous Underwater Vehicle Technology, 399416.Google Scholar
Moreira, L. and Soares, C.G. (2008). H2 and H∞ Designs for diving and course control of an autonomous underwater vehicle in presence of waves. IEEE Journal of Oceanic Engineering, 33(2), 6988.Google Scholar
Petrich, J. and Stilwell, D.J. (2011). Robust control for an autonomous underwater vehicle that suppresses pitch and yaw coupling. Ocean Engineering, 38(1), 197204.Google Scholar
Saravanakumar, S. and Asokan, T. (2013). Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space. Intelligent Service Robotics, 6(4), 211224.Google Scholar
Rimon, E. and Koditschek, D.E. (1992). Exact robot navigation using artificial potential functions, Robotics and Automation. IEEE Transactions on Robotics and Automation, 8(5), 501518.Google Scholar
Yin, L. and Yin, Y. (2008). An improved potential field method for mobile robot path planning in dynamic environments. Proceedings of the 7th World Congress on Intelligent Control and Automation, 48474852.Google Scholar