Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Simultaneous Planning and Control Method Integrating APF and MPC to Solve Autonomous Navigation for USVs in Unknown Environments

  • Short Paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper is devoted to solving autonomous navigation for unmanned surface vessels (USVs) in unknown environments. To overcome the deficiency of the “first planning then tracking” motion control framework, a novel simultaneous planning and control (SPC) method is developed. The developed method combines an improved artificial potential field (IAPF) and model predictive control (MPC) techniques. Improvements in the IAPF are made to deal with constraints on angular velocity. In each step of the SPC method, the IAPF is used for robust and efficient tracking in a short future. And the MPC is implemented to generate actual control commands for high-precision tracking. The IAPF and the MPC work in an alternative way to drive the USV to the prescribed target while avoiding the obstacles detected around. Simulations with static and dynamic obstacles demonstrate the effectiveness of the proposed method. The method works well when maneuvering in complex environments even crossing narrow tunnels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Code Availability

Not applicable.

References

  1. Wang, N., Jin, X., Er, M.J.: A multilayer path planner for a USV under complex marine environments. Ocean Eng. 184(15), 1–10 (2019)

    Article  Google Scholar 

  2. Cai, W., Zhang, M., Zheng, Y.: Task assignment and path planning for multiple autonomous underwater vehicles using 3D Dubins curves. Sensors 17(7), 1607 (2017)

    Article  Google Scholar 

  3. Niu, H., et al.: Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles. J. Navig. 72(4), 125 (2019)

    Article  Google Scholar 

  4. Niu, H., et al.:Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment. Ocean Eng. 196(15), 106766.1–106766.14 (2020)

    Google Scholar 

  5. Shah Brual, C., Gupta, S.K.: Long-distance path planning for unmanned surface vehicles in complex marine environment. IEEE J. Oceanic Eng. 45(3), 813-830 (2020)

    Article  Google Scholar 

  6. Wang, H., et al.: Collision avoidance planning method of usv based on improved ant colony optimization algorithm. IEEE Access 7, 52964–52975 (2019)

  7. Xin, J., et al.: An improved genetic algorithm for path-planning of unmanned surface vehicle. Sensors 19(11), 2640 (2019)

    Article  Google Scholar 

  8. Guo, S., et al.: An autonomous path planning model for unmanned ships based on deep reinforcement learning. Sensors 20(2), 426 (2020)

    Article  Google Scholar 

  9. Fu, M., et al.: Multi-behavior fusion based potential field method for path planning of unmanned surface vessel. China Ocean Eng. 33(5), 80–89 (2019)

    Article  Google Scholar 

  10. Song, J., Hao, C., Su, J.: Path planning for unmanned surface vehicle based on predictive artificial potential field. Int. J. Adv. Robot. Syst. 17(2), (2020)

  11. Lu, H.: COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields. J. Navig. 72(3), 1–21 (2018)

    Google Scholar 

  12. Wang, N., Xu, H.: Dynamics-constrained global-local hybrid path planning of an autonomous surface vehicle. IEEE Trans. Veh. Technol. 69(7), 6928–6942 (2020)

    Article  Google Scholar 

  13. Zhang, Z., Zhang, S., Li, H., Yan, W.: Cooperative robust optimal control of uncertain multi-agent systems. J. Franklin Inst.-Eng. Appl. Math. 357(14), 9467–9483 (2020)

  14. Chen, Y., Lee, C., Tseng, S., Hu, W. Nonlinear Optimal control law of autonomous unmanned surface vessels. Appl. Sci.-Basel 10(5), 1686 (2020)

  15. Dong, Z., Wan, L., Li, Y., Liu, T., Zhang, G.: Trajectory tracking control of underactuated USV based on modified backstepping approach. Int. J. Naval Architecture Ocean Eng. 7(5), 817–832 (2015)

    Article  Google Scholar 

  16. Wen, G., et al.: Adaptive tracking control of surface vessel using optimized backstepping technique. IEEE Trans. Cybern. 49(9), 3420–3431 (2019)

    Article  Google Scholar 

  17. Zhang, J., Yang, G.: Fault-tolerant fixed-time trajectory tracking control of autonomous surface vessels with specified accuracy. IEEE Trans. Industr. Electron. 67(6), 4889 (2020)

    Article  MathSciNet  Google Scholar 

  18. Wang, N., Pan, X., Su, S.-F.: Finite-time fault-tolerant trajectory tracking control of an autonomous surface vehicle. J. Franklin Inst. 357(16), 11114–11135 (2020)

    Article  MathSciNet  Google Scholar 

  19. Zhang, J., Yu, S., Wu, D., Yan, Y.: Nonsingular fixed-time terminal sliding mode trajectory tracking control for marine surface vessels with anti-disturbances. Ocean Eng. 217, 108158 (2020)

  20. Qiu, B., Wang, G., Fan, Y., Mu D., Sun, X.: Adaptive sliding mode trajectory tracking control for unmanned surface vehicle with modeling uncertainties and input saturation. Appl. Sci. 9(6), 1240 (2019)

  21. Qin, H., Li, C., Sun, Y.: Adaptive neural network-based fault-tolerant trajectory-tracking control of unmanned surface vessels with input saturation and error constraints. IET Intel. Transport Syst. 14(5), 356–363 (2020)

    Article  Google Scholar 

  22. Deng, Y., Zhang, X., Im, N., Zhang, G., Zhang, Q.: Adaptive fuzzy tracking control for underactuated surface vessels with unmodeled dynamics and input saturation. ISA Trans. 103, 52–62 (2020)

    Article  Google Scholar 

  23. Dong, C., Ye, Q., Dai, S.-L.: Neural-network-based adaptive output-feedback formation tracking control of USVs under collision avoidance and connectivity maintenance constraints. Neurocomputing 401, 101–112 (2020)

    Article  Google Scholar 

  24. Zheng, Z., Ruan, L., Zhu, M., Guo, X.: Reinforcement learning control for underactuated surface vessel with output error constraints and uncertainties. Neurocomputing 399, 479–490 (2020)

    Article  Google Scholar 

  25. Abdelaal, M., Fränzle, M., Hahn, A.: Nonlinear Model Predictive Control for trajectory tracking and collision avoidance of underactuated vessels with disturbances. Ocean Eng. 160, 168–180 (2018)

    Article  Google Scholar 

  26. Yao, X., Wang, X., Zhang, L., Jiang, X.: Model predictive and adaptive neural sliding mode control for three-dimensional path following of autonomous underwater vehicle with input saturation. Neural Comput. Appl. 32(22), 16875–16889 (2020)

    Article  Google Scholar 

  27. Zhu, D., Mei, M., Sun, B.: The tracking control of unmanned underwater vehicles based on model predictive control. Int. J. Robot. Autom. 32(4), 351–359 (2017)

    Google Scholar 

  28. Bergman, K., Ljungqvist, O., Linder, J., Axehill, D.: An optimization-based motion planner for autonomous maneuvering of marine vessels in complex environments. 59th IEEE Conference on Decision and Control (CDC), (2020)

  29. Bergman, K., Ljungqvist, O., Linder, J., Axehill, D.: A COLREGs-compliant motion planner for autonomous maneuvering of marine vessels in complex environments. arXiv:2012.12145v2, (2021)

  30. Bitar, G., Martinsen, A., Lekkas, A., Breivik, M.: Two-stage optimized trajectory planning for asvs under polygonal obstacle constraints: theory and experiments. IEEE Access 8, 199953–199969 (2020)

    Article  Google Scholar 

  31. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986)

    Article  Google Scholar 

  32. Garg, D., et al.: A unified framework for the numerical solution of optimal control problems using pseudospectral methods. Automatica 46(11), 1843–1851 (2010)

    Article  MathSciNet  Google Scholar 

  33. Gottlieb, D.: The stability of pseudospectral-chebyshev methods. Math. Comput. 36(153), 107–118 (1981)

    Article  MathSciNet  Google Scholar 

  34. Bayliss, A., Kuske, R., Matkowsky, B.J.: A two-dimensional adaptive pseudo-spectral method. J. Comput. Phys. 91(1), 174-196 (1990)

    Article  MathSciNet  Google Scholar 

  35. Peng, H., et al.: An iterative symplectic pseudospectral method to solve nonlinear state-delayed optimal control problems. Commun. Nonlinear Sci. Numer. Simul. 48, 95–114 (2017)

    Article  MathSciNet  Google Scholar 

  36. Wang, X., et al.: A symplectic pseudospectral method for nonlinear optimal control problems with inequality constraints. ISA Trans. 68, 335–352 (2017)

    Article  Google Scholar 

  37. Wang, X., et al.: A review on carrier aircraft dispatch path planning and control on deck. Chin. J. Aeronaut. 33(12), 3039–3057 (2020)

    Article  Google Scholar 

  38. Wang, X., Liu, J., Peng, H.: Symplectic pseudospectral methods for optimal control: theory and applications in path planning. Springer (2021)

  39. Wang, X., et al.: Input-constrained chaos synchronization of horizontal platform systems via a model predictive controller. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 235(20), 4862–4872 (2021)

  40. Wang, X., Liu, J., et al.: A unified symplectic pseudospectral method for motion planning and tracking control of 3D underactuated overhead cranes. Int. J. Robust Nonlinear Control 29(7), 2236–2253 (2019)

  41. Liu, J., et al.: Trajectory planning and tracking control for towed carrier aircraft system. Aerosp. Sci. Technol. 84, 830–838 (2019)

    Article  Google Scholar 

Download references

Funding

The authors are grateful for the National Key Research and Development Plan (2019YFB1706502); the National Natural Science Foundation of China (12102077, 62003366); the Fundamental Research Funds for the Central Universities (DUT22RC(3)010).

Author information

Authors and Affiliations

  1. Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Xinwei Wang & Haijun Peng

  2. War Research Institute, Academy of Military Sciences, Beijing, 100850, China

    Jie Liu

  3. Baimtec Materials Co., Ltd, No. 5 Yongxiang North Road, Haidian District, 100094, Beijing, China

    Xiwang Qie

  4. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

    Xudong Zhao

  5. School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China

    Chen Lu

Authors
  1. Xinwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haijun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiwang Qie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xudong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chen Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Wang X and Liu J contributed to project conceptualisation. Wang X, Peng H and Zhao X built the model. Wang X, Qie X and Lu C designed the scenario settinsgs. Wang X and Liu J wrote the manuscript. Liu J, Qie X and Lu C edited the manuscript. Liu J supervised the project. All authors approved the manuscript.

Corresponding author

Correspondence to Jie Liu.

Ethics declarations

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to Participate

All authors agreed to participate the research.

Consent for Publication

All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical collection on Unmanned Systems

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Liu, J., Peng, H. et al. A Simultaneous Planning and Control Method Integrating APF and MPC to Solve Autonomous Navigation for USVs in Unknown Environments. J Intell Robot Syst 105, 36 (2022). https://doi.org/10.1007/s10846-022-01663-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01663-8

Keywords

Search

Navigation