Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T05:02:39.221Z Has data issue: false hasContentIssue false
  • English
  • Français

Agile collision avoidance for unmanned surface vehicles based on collision shielded model prediction control algorithm

Published online by Cambridge University Press:  18 July 2022

Yihan Tao Open the ORCID record for Yihan Tao [Opens in a new window]  and
Jialu Du Open the ORCID record for Jialu Du [Opens in a new window]
Yihan Tao
Affiliation:
School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Jialu Du*
Affiliation:
School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
*
*Corresponding author. E-mail: dujl@dlmu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Collision avoidance (COLAV) is a prerequisite for the navigation safety of unmanned surface vehicles (USVs). Since USVs have to avoid obstacles clearly and timely, i.e. the COLAV should be agile, the COLAV algorithm should have low computation complexity and make efficient COLAV decisions. However, balancing between the computation complexity and the COLAV decision optimality is still intractable at present. This paper innovatively proposes a COLAV algorithm for USVs by combining the velocity obstacle method with the predictive model method, named the collision shielded model predictive control (CS-MPC) algorithm, such that the agility of USVs COLAV is improved. The runtime of the proposed COLAV algorithm is shortened by shielding the dangerous parts of the search space of the COLAV decisions, and the COLAV decision is efficient with the aid of the accurately predicted motion trajectory by the motion mathematical model of USVs. As such, the USV can safely navigate in complex water areas where multiple vessels and obstacles exist. A series of simulations on a yacht in different kinds of encounter situations were carried out to verify the effectiveness and the agility of the proposed CS-MPC COLAV algorithm.

Keywords

Unmanned surface vehicleautonomousship collision
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 5 , September 2022 , pp. 1243 - 1267
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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

Benjamin, M. R., Leonard, J. J., Curcio, J. A. and Newman, P. M. (2006). A method for protocol-based collision avoidance between autonomous marine surface craft. Journal of Field Robotics, 23(5), 333–346.10.1002/rob.20121CrossRefGoogle Scholar
Brock, O. and Khatib, O. (1999). High-speed Navigation Using the Global Dynamic Window Approach. Proceedings of the 1999 IEEE International Conference on Robotics and Automation, Detroit, USA.CrossRefGoogle Scholar
Chiang, H. L. and Tapia, L. (2018). COLREG-RRT: An RRT-based COLREGS-compliant motion planner for surface vehicle navigation. IEEE Robotics and Automation Letters, 3, 2024–2031.10.1109/LRA.2018.2801881CrossRefGoogle Scholar
Enevoldsen, T. T., Reinartz, C. and Galeazzi, R. (2021). COLREGs-Informed RRT* for Collision Avoidance of Marine Crafts. Proceedings of 2021 IEEE International Conference on Robotics and Automation, Xi'an, China.CrossRefGoogle Scholar
Eriksen, B. O. H. (2019). Collision avoidance and motion control for autonomous surface vehicles. Ph.D. Thesis, Norwegian University of Science and Technology.Google Scholar
Eriksen, B. O. H, Wilthil, E. F., Flaten, A. L., Brekke, E. F. and Breivik, M. (2018). Radar-based Maritime Collision Avoidance Using Dynamic Window. Proceedings of the 2018 IEEE Aerospace Conference, Big Sky, USA.CrossRefGoogle Scholar
Fiorini, P. and Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 17(7), 760–772.CrossRefGoogle Scholar
Fossen, T. I. (2011). Handbook of Marine Craft Hydrodynamics and Motion Control. United Kingdom: John Wiley & Sons, Ltd. Chichester.CrossRefGoogle Scholar
Fox, D., Burgard, W. and Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4(1), 23–33.CrossRefGoogle Scholar
Hagen, I. B., Kufoalor, D. K. M., Brekke, E. F. and Johansen, T. A. (2018). MPC-based Collision Avoidance Strategy for Existing Marine Vessel Guidance Systems. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.CrossRefGoogle Scholar
Huang, Y. M., Chen, L. Y. and van Gelder, P. H. A. J. M. (2019). Generalized velocity obstacle algorithm for preventing ship collisions at sea. Ocean Engineering, 173, 142–156.CrossRefGoogle Scholar
Huang, Y. M., Chen, L. Y., Negenborn, R. R. and van Gelder, P. H. A. J. M. (2020). A ship collision avoidance system for human-machine cooperation during collision avoidance. Ocean Engineering, 217, 107913.CrossRefGoogle Scholar
James, M. K. (1986). Modelling the decision process in computer simulation of ship navigation. The Journal of Navigation, 39, 32–48.CrossRefGoogle Scholar
Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5(1), 90–98.CrossRefGoogle Scholar
Kiss, D. and Tevesz, G. (2012). Advanced Dynamic Window Based Navigation Approach Using Model Predictive Control. Proceedings of the 17th International Conference on Methods & Models in Automation & Robotics (MMAR), Miedzyzdroje, Poland.CrossRefGoogle Scholar
Kongsberg Maritime. (2021). Autonomous Ships. Available at: https://www.kongsberg.com/maritime/solutions/ship-types/autonomous-ships (Accessed 17 Jan. 2021).Google Scholar
Kreutzmann, A., Wolter, D., Dylla, F. and Lee, J. H. (2013). Towards safe navigation by formalizing navigation rules. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 7(2), 161–168.10.12716/1001.07.02.01CrossRefGoogle Scholar
Kufoalor, D. K. M., Brekke, E. F. and Johansen, T. A. (2018). Proactive Collision Avoidance for ASVS Using A Dynamic Reciprocal Velocity Obstacles Method. Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.10.1109/IROS.2018.8594382CrossRefGoogle Scholar
Kufoalor, D. K. M., Wilthil, E., Hagen, I. B., Brekke, E. F. and Johansen, T. A. (2019). Autonomous COLREGs-Compliant Decision Making Using Maritime Radar Tracking and Model Predictive Control. Proceedings of the 18th European Control Conference (ECC), Naples, Italy.10.23919/ECC.2019.8796273CrossRefGoogle Scholar
Kufoalor, D. K. M., Johansen, T. A., Brekke, E. F., Hepsø, A. and Trnka, K. (2020). Autonomous maritime collision avoidance: Field verification of autonomous surface vehicle behavior in challenging scenarios. Journal of Field Robotics, 37, 387–403.CrossRefGoogle Scholar
Kuwata, Y., Wolf, M. T., Zarzhitsky, D. and Huntsberger, T. L. (2014). Safe maritime autonomous navigation with COLREGs, using velocity obstacles. IEEE Journal of Oceanic Engineering, 39(1), 110–119.10.1109/JOE.2013.2254214CrossRefGoogle Scholar
Larson, J., Bruch, M. and Ebken, J. (2006). Autonomous Navigation and Obstacle Avoidance for Unmanned Surface Vehicles. Proceedings of SPIE Unmanned Systems Technology VIII, Defense and Security Symposium, Orlando, USA.10.1117/12.663798CrossRefGoogle Scholar
Levander, O. (2017). Autonomous ships on the high seas. IEEE Spectrum, 54(2), 26–31.CrossRefGoogle Scholar
Liu, Z. X., Zhang, Y. M., Yu, X. and Yuan, C. (2016). Unmanned surface vehicles: An overview of developments and challenges. Annual Reviews in Control, 41, 71–93.10.1016/j.arcontrol.2016.04.018CrossRefGoogle Scholar
Loe, Ø. A. G. (2008). Collision avoidance for unmanned surface vehicles. Master's thesis, Norwegian University of Science and Technology. Trondheim, Norway.Google Scholar
Lyu, H. and Yin, Y. (2018). COLREGs-Constrained real-time path planning for autonomous ships using modified artificial potential fields. The Journal of Navigation, 72(3), 1–21.Google Scholar
Martinezgomez, L. and Fraichard, T. (2009). Collision Avoidance in Dynamic Environments: An ICS-Based Solution and its Comparative Evaluation. Proceedings of the IEEE International Conference on Robotics & Automation, Kobe, Japan.10.1109/ROBOT.2009.5152536CrossRefGoogle Scholar
Moe, S. and Pettersen, K. Y. (2017). Set-Based Line-of-Sight (LOS) Path Following with Collision Avoidance for Underactuated Unmanned Surface Vessels Under the Influence of Ocean Currents. Proceedings of the IEEE Conference on Control Technology & Applications, Maui, USA.10.1109/CCTA.2017.8062470CrossRefGoogle Scholar
Open Robotics. (2021). The Robot Operating System. Available at: https://www.ros.org/ (Accessed 17 Jan. 2021).Google Scholar
Perera, L. P., Carvalho, J. P. and Soares, C. G. (2011). Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. Journal of Marine Science & Technology, 16(1), 84–99.CrossRefGoogle Scholar
Richalet, J., Rault, A., Testud, J. L. and Papon, J. (1978). Model predictive heuristic control: Applications to an industrial process. Automatica, 14, 413–428.10.1016/0005-1098(78)90001-8CrossRefGoogle Scholar
Savkin, A. V. and Wang, C. (2013). A simple biologically inspired algorithm for collision-free navigation of a unicycle-like robot in dynamic environments with moving obstacles. Robotica, 31(6), 993–1001.10.1017/S0263574713000313CrossRefGoogle Scholar
Snape, J., Berg, J., Guy, S. J. and Manocha, D. (2011). The hybrid reciprocal velocity obstacle. IEEE Transactions on Robotics, 27(4), 696–706.CrossRefGoogle Scholar
Stenersen, T. (2015). Guidance system for autonomous surface vehicles. Master thesis, Norwegian University of Science and Technology, Trondheim, Norway.Google Scholar
Wiig, M. S., Pettersen, K. Y. and Krogstad, T. R. (2019). Collision avoidance for underactuated marine vehicles using the constant avoidance angle algorithm. IEEE Transactions on Control Systems Technology, 28(3), 951–966.CrossRefGoogle Scholar
Yang, R. (2019). Research on COLREGs-compliant shipborne autonomous collision avoidance system. Shanghai, China: Ph.d. thesis, Shanghai Jiao Tong University.Google Scholar
Zheng, Z. (2000). Research on automatic decision-making system of vessel collision avoidance. Dalian, China: Ph.d. thesis, Dalian Maritime University.Google Scholar
Zhuang, J., Su, Y., Liao, Y. and Sun, H. (2011). Motion planning of USV based on marine rules. Procedia Engineering, 15, 269–276.CrossRefGoogle Scholar