||Research on Autonomous Underwater Vehicle Collision Avoidance and Navigation Based on H∞ Controller and Artificial Potential Field Method
||Department of Systems and Naval Mechatronic Engineering
autonomous underwater vehicle
artificial potential field method
obstacle avoidance and navigation
本論文提出H∞控制器結合人工勢場法(artificial potential field method, APFM)以解決自主式水下載具(autonomous underwater vehicle, AUV)避碰與導航問題，我們應用人工勢場法設計了深度、高度及航向等三種控制演算法，以實現AUV在未知三維靜態環境之障礙物避碰與導航控制；在深度控制演算法中，將載具的安全高度納入考量，以避免AUV碰撞到海底或垂向障礙物；在高度控制演算法中，將載具的最大安全深度納入考量，以避免AUV過度跟隨海底地形，而超出載具可承受最大壓力的安全深度；而航向控制演算法是基於改良式人工勢場法，除了解決人工勢場法局部極小值問題之外，並結合障礙物邊緣跟隨法(obstacle boundary following method, OBFM)解決了AUV落入U型陷阱及重覆路徑徘徊等問題。
在模擬分析上，以自行開發的自主式水下載具NCKU-AUV作為研究對象，經由平面運動機構(planar motion mechanism, PMM)實驗取得了載具的流體動力係數做為系統模擬參數，建立數學模型設計了H∞控制器，並以迴路整型方法調整權重函數，獲取最佳化控制器，經由模擬結果得知該控制器具有強健性及抗干擾性，滿足系統性能要求及穩定性；另外，亦對於深度、高度及航向等控制演算法進行模擬，在不同深淺的海底地形及障礙物狀況假設下，經模擬實驗結果得知，所提出的避碰控制演算法的有效性，正確地引導AUV避開障礙物安全地潛航，且在H∞控制器的執行下準確地導航到達指定目標。
This dissertation proposes integrating an H∞ controller with an artificial potential field method (APFM) to solve collision avoidance and navigation issues in an autonomous underwater vehicle (AUV). We applied APFM in designing three types of control algorithms—altitude, depth, and heading; the AUV used the proposed control algorithms to navigate in unknown three-dimensional static environment and avoid collisions with obstacles. The depth-control algorithm involved a safe altitude above the seafloor to prevent the AUV from colliding with the seafloor or vertical obstacles. The altitude-control algorithm involved a maximum safe depth below the surface to prevent the AUV from following terrain beyond the maximum pressure of vessel strength. The heading-control algorithm was based on an improved APFM to solve the local minimum problem; it combined APFM with an obstacle boundary following method (OBFM) to solve the problems of the AUV falling into the U-shaped trap and repetition path hovering.
For simulation analysis, we modeled a self-developed National Cheng Kung University Autonomous Underwater Vehicle (NCKU-AUV) as a device under test. Using a planar motion mechanism (PMM) test, we obtained the hydrodynamic force coefficient of the vehicle. We applied the system simulation parameters for building the mathematical model used to design the H∞ controller. We used the loop shaping method to adjust the weight function, obtaining the optimal controller. The simulation results showed that the controller had robustness and anti-interference properties, met system performance requirements, and provided stability. Additionally, we tested the control algorithms for altitude, depth, and heading, simulating seafloor with different depths, different terrain, and various types of obstacles; the simulation results showed that the proposed collision avoidance algorithm was effective and guided the NCKU-AUV to avoid the underwater obstacles safely and correctly. Using the simulated H∞ controller, the simulated NCKU-AUV was able to accurately navigate to the appointed target.
For practical testing, physical sensors including a Doppler velocity log, depth gauge, altimeter, and inertia measurement unit were installed on the physical NCKU-AUV to measure its velocity, altitude, depth, and attitude angle. In particular, five sonar sensors were installed at the bow end of the vehicle to detect horizontal and vertical obstacles; these sonar sensors were able to measure the distances between the vehicle and obstacles. AUV trials were carried out in the towing tank at NCKU. Obstacles were placed at the bottom of the towing tank, and the walls of the tank acted as a horizontal U-shaped obstacle. All control tests (fixed altitude, fixed depth, and navigation) were conducted and completed successfully. Preliminary results of the towing tank test validated the feasibility and effectiveness of the proposed H∞ controller with APFM; real sea testing could be conducted in future to prove the practicality of this system.
Table of Contents VII
List of Tables X
List of Figures XI
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Literature Review 4
1.3 Research Purpose 6
1.4 NCKU-AUV Description 6
1.4.1 Hardware Architecture 8
1.4.2 Software Architecture 10
1.5 Organization of Dissertation 12
Chapter 2 Research Methods 13
2.1 Mathematical Model 13
2.1.1 Coordinate System 13
2.1.2 AUV Kinematics 14
2.1.3 AUV Dynamics 15
2.2 Artificial Potential Field Method (APFM) 25
2.2.1 Basic principle of APFM 25
2.2.2 Local Minima Problem 27
2.3 Improved Artificial Potential Field Method (IAPFM) 28
2.4 H∞ Control Theory 30
2.4.1 Control Theory 30
2.4.2 Loop Shaping 35
2.4.3 Uncertainties 39
2.4.4 Sensitivity Matrix 45
2.4.5 Selecting Weighting Functions 47
2.4.6 Weighting Functions versus Loop Shaping 50
2.4.7 H∞ Controller Search Procedure 57
2.5 AUV Autopilot Controller 58
2.5.1 Depth-control Algorithm 58
2.5.2 Altitude-control Algorithm 61
2.5.3 Heading-control Algorithm 63
Chapter 3 Simulation Analysis 69
3.1 Model Simplifications 69
3.1.1 Diving-control System 69
3.1.2 Steering-control System 70
3.2 Performance Analysis 72
3.2.1 Frequency-domain Response 72
3.2.2 Time-domain Response 75
3.2.3 Interference Noise Simulation 78
3.2.4 Path Tracking 82
3.3 Collision Avoidance Control Simulations 87
3.3.1 Depth-control Simulation 87
3.3.2 Altitude-control Simulation 90
3.3.3 Heading-control Simulation 93
Chapter 4 Experimental Results 96
4.1 Configuration of Altimeters 96
4.2 Bathymetric Measurement 97
4.3 Depth-control Trial 98
4.4 Altitude-control Trial 100
4.5 Heading-control Trial 101
4.5.1 Heading Angle-tracking Test 101
4.5.2 Cross-tracking Control Test 103
4.5.3 Obstacle Boundary–detection Test 104
4.5.4 U-shaped Type I Obstacle-avoidance Test 106
4.5.5 U-shaped Type II Obstacle-avoidance Test 108
Chapter 5 Conclusions and Future Work 110
Appendix A H∞ variational approach 118
Appendix B Kalman filter design and DVL measurement 126
Appendix C Obstacle Detection Technique 133
Antonelli, G., S. Chiaverini, R. Finotello, and R. Schiavon (2001), “Real-time path planning and obstacle avoidance for RAIS: an autonomous underwater vehicle,” IEEE Journal of Oceanic Engineering, Vol. 26, No. 2, pp. 216-227.
Cheng, C.L., D.Q. Zhu, B. Sun, Z.Z. Chu, J.D. Nie, and S. Zhang (2015), “Path planning for autonomous underwater vehicle based on artificial potential field and velocity synthesis,” IEEE 28th Canadian Conference on Electrical and Computer Engineering, pp.717-721.
Creuze, V. and B. Jouvencel (2002), “Avoidance of underwater cliffs for autonomous underwater vehicles,” Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 793-798.
Cristi, R. and A. Healey (1989), “Adaptive identification and control of an autonomous underwater vehicle,” Proc. 6th Int. Symp. Unmanned Untethered Submersible Technology, pp. 563-572.
Ding, F.G., P. Jiao, X.G. Bian, and H.J. Wang (2005), “AUV local path planning based on virtual potential field,” Proceedings of the IEEE International Conference on Mechatronics & Automation, Vol. 4, pp.1711-1716.
Doyle, J.C., K. Glover, P.P. Khargoner, and B.A. Francis (1989), “State-space solution to standard H2 and H control problem,” IEEE Trans. Automatic Control, Vol. 34, No. 8, pp. 831-847.
Fang, M.C., S.M. Wang, M.C. Wu, and Y.H. Lin (2015), “Applying the self-tuning fuzzy control with the image detection technique on the obstacle-avoidance for autonomous underwater vehicles,” Ocean Engineering, Vol. 93, pp.11-24.
Feng, Z. and R. Allen (2002), “H∞ autopilot design for an autonomous underwater vehicle,” Proceedings of the 2002 International Conference on Control Applications, pp. 350-354.
Francis, B.A. (1987), “Lecture notes in control and information sciences - a course in H control theory,” Springer-Verlag.
Gao, J., D. Xu, N. Zhao, and W. Yan (2008), “A potential field method for bottom navigation of autonomous underwater vehicles,” Proceedings of the 7th World Congress on Intelligent Control and Automation, pp. 7466-7470.
Gao, Y., Z.Q. Wei, F.X. Gong, B. Yin, and X.P. Ji (2013), “Dynamic path planning for underwater vehicles based on modified artificial potential field method,” Fourth International Conference on Digital Manufacturing & Automation, pp. 518-521.
Ge, S.S. and Y.J. Cui (2000), “New potential functions for mobile robot path planning,” IEEE Transactions on Robotics and Automation, Vol. 16, No. 5, pp. 615-620.
Ge, S.S. and Y.J. Cui (2002), “Dynamic motion planning for mobile robots using potential field method,” Autonomous Robots, Vol. 13, pp. 207-222.
Hanumant, S. (1995), “Sonar mapping with the autonomous benthic explorer (ABE),” Proceedings of the 9th International Symposium on Unmanned Untethered Submersible Technology, pp. 367-375.
Healey, A.J. (2004), “Obstacle avoidance while bottom following for the REMUS autonomous underwater vehicle,” Proceedings of the IFAC Conference, Lisbon, Portugal, July 5-7.
Horner, D.P., A.J. Healey, and S.P. Kragelund (2005), “AUV experiments in obstacle avoidance,” Proc. of the MTS/IEEE OCEANS, pp. 1464-1470.
Hwang, C.N. (1993), “Formulation of H2 and H∞ optimal control problems—a variational approach,” Journal of the Chinese Institute of Engineers, Vol. 16, No. 6.
Kaminer, I., A.M. Pascoal, C.J. Silvestre, and P.P. Khargonekar (1991), “Control of an underwater vehicle using H-infinity synthesis,” Proceedings of the 30th IEEE Conference on Decision and Control, pp. 2350-2355.
Karabchevsky, S., B. Braginsky, and H. Guterman (2012), “AUV real-time acoustic vertical plane obstacle detection and avoidance,” 2012 IEEE/OES, pp. 1-6.
Kato, N., Y. Ito, J. Kojima, K. Asakawa, and Y. Shirasaki (1994), “Control performance of autonomous underwater vehicle, AQUA explorer 1000, for inspection of underwater cables,” Proc. IEEE Conf. OCEANS, pp. 135-140.
Khatib, O. (1986), “Real-time obstacle avoidance for manipulators and mobile robots,” International Journal of Robotics Research, Vol. 5, No. 1, pp. 90-98.
Khosla, P. and R. Volpe (1988), “Superquadratic artificial potentials for obstacle avoidance and approach,” Proceedings of the IEEE Conference on Robotics and Automation, pp. 1778-1784.
Kim, J., K. Lee, Y. Cho, H. Lee, and H. Park (2000), “Mixed H2/H∞ control with regional pole placements for underwater vehicle systems,” Proc. American Control Conf., pp. 80-84.
Koren, Y. and J. Borenstein (1991), “Potential field methods and their inherent limitations for mobile robot navigation,” IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1398-1404.
Leedekerken, J.C., J.J. Leonard, M.C. Bosse, and A. Balasuriya (2006), “Real-time obstacle avoidance and mapping for AUVs operating in complex environments,” Proceedings of the 7th International Mine Warfare Symposium, Monterey, CA.
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, pp. 399-416.
Martin, A., A. An, K. Nelson, and S. Smith (2000), “Obstacle detection by a forward looking sonar integrated in an autonomous underwater vehicle,” OCEANS 2000 MTS/IEEE Conference and Exhibition, Vol. 1, pp. 337-341.
McPhail, S., M. Furlong, and M. Pebody (2010), “Low-altitude terrain following and collision avoidance in a flight-class autonomous underwater vehicle,” Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment, Vol. 224, No. 4, pp. 279-292.
Moreira, L. and C.G. Soares (2008), “H2 and H∞ designs for diving and course control of an autonomous underwater vehicle in presence of waves,” IEEE Journal of Oceanic Engineering, Vol. 33, No. 2, pp. 69-88.
Pebody, M. (2008), “Autonomous underwater vehicle collision avoidance for under-ice exploration,” Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, Vol. 222, No. 2, pp. 53-66.
Petrich, J. and D.J. Stilwell (2011), “Robust control for an autonomous underwater vehicle that suppresses pitch and yaw coupling,” Ocean Engineering, Vol. 38, No. 1, pp. 197-204.
Quidu, I., A. Hetet, Y. Dupas, and S. Lefevre (2007), “AUV (Redermor) obstacle detection and avoidance experimental evaluation,” OCEANS 2007, pp. 1-6.
Saravanakumar, S. and T. Asokan (2013), “Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space,” Intelligent Service Robotics, Vol. 6, No. 4, pp. 211-224.
Wang, S.M., Z.H. Chen, and C.N. Hwang (2016), “The composite design of H∞-ERL sliding-mode controller,” The Journal of Marine Science and Technology, Vol. 24, No. 3, pp. 562-574.
Williams, G.N., G.E. Lagace, and A. Woodfin (1990), “A collision avoidance controller for autonomous underwater vehicles,” Proc. IEEE Symp. Autonomous Underwater Vehicle Technology, pp. 206-212.
Yin, L. and Y. Yin (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, pp. 4847-4852.
Yoerger, D. and J. Slotine (1985), “Robust trajectory control of underwater vehicles,” IEEE Journal of Oceanic Engineering, Vol. 10, No. 4, pp. 462-470.
Yuh, J. (1990), “Modeling and control of underwater robotic vehicles,” IEEE Trans. Syst., Man., Cybern., Vol. 20, No. 6, pp. 1476-1483.