Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

[1]  P.H.A.J.M. van Gelder,et al.  An improved time discretized non-linear velocity obstacle method for multi-ship encounter detection , 2020 .

[2]  Gary G. Yen,et al.  A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management , 2020, Appl. Soft Comput..

[3]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[4]  Yamin Huang,et al.  Comparison between the collision avoidance decision-making in theoretical research and navigation practices , 2021 .

[5]  Asgeir J. Sørensen,et al.  A Voronoi-diagram-based dynamic path-planning system for underactuated marine vessels☆ , 2017 .

[6]  Junmin Mou,et al.  A Velocity Obstacle-Based Real-Time Regional Ship Collision Risk Analysis Method , 2021, Journal of Marine Science and Engineering.

[7]  Nirmalya Chowdhury,et al.  Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets , 2020, J. King Saud Univ. Comput. Inf. Sci..

[8]  Pieter van Gelder,et al.  Global path planning for autonomous ship: A hybrid approach of Fast Marching Square and velocity obstacles methods , 2020 .

[9]  Sanjay Sharma,et al.  A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents , 2018, Ocean Engineering.

[10]  Chinthaka Premachandra,et al.  3D environment mapping and self-position estimation by a small flying robot mounted with a movable ultrasonic range sensor , 2017 .

[11]  Maarten van Someren,et al.  Machine learning for vessel trajectories using compression, alignments and domain knowledge , 2012, Expert Syst. Appl..

[12]  Jian He,et al.  An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost , 2021, Journal of Marine Science and Engineering.

[13]  Hiroharu Kawanaka,et al.  A study on hovering control of small aerial robot by sensing existing floor features , 2020, IEEE/CAA Journal of Automatica Sinica.

[14]  Antonios Tsourdos,et al.  Voronoi-Visibility Roadmap-based Path Planning Algorithm for Unmanned Surface Vehicles , 2019, Journal of Navigation.

[15]  Junmin Mou,et al.  Improved kinematic interpolation for AIS trajectory reconstruction , 2021 .

[16]  Chinthaka Premachandra,et al.  Development of an Automated Camera-Based Drone Landing System , 2020, IEEE Access.

[17]  Pengfei Chen,et al.  Quantitative analysis of COLREG rules and seamanship for autonomous collision avoidance at open sea , 2017 .

[18]  Xiaojian Liu,et al.  Experimental and numerical investigations of advancing speed effects on hydrodynamic derivatives in MMG model, part I: Xvv,Yv,Nv , 2019, Ocean Engineering.

[19]  Quan Zhou,et al.  Parallel trajectory planning for shipborne Autonomous collision avoidance system , 2019, Applied Ocean Research.

[20]  Xiaoyuan Wang,et al.  Local path optimization method for unmanned ship based on particle swarm acceleration calculation and dynamic optimal control , 2021 .

[21]  Halpage Chinthaka Nuwandika Premachandra,et al.  Improving landmark detection accuracy for self-localization through baseboard recognition , 2017, Int. J. Mach. Learn. Cybern..

[22]  Chen Pengfei,et al.  Mechanism of dynamic automatic collision avoidance and the optimal route in multi-ship encounter situations , 2020 .

[23]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[24]  Euiho Kim,et al.  Hybrid path planning using positioning risk and artificial potential fields , 2021 .

[25]  I. Parberry,et al.  Optimal Interceptions on Two-Dimensional Grids with Obstacles , 2007, Journal of Navigation.

[26]  Songhao Piao,et al.  Robot path planning by leveraging the graph-encoded Floyd algorithm , 2021, Future Gener. Comput. Syst..

[27]  Thor I. Fossen,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control , 2011 .

[28]  Arturo de la Escalera,et al.  Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles , 2018 .

[29]  Lei Xie,et al.  A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters , 2019, Ocean Engineering.

[30]  Oleg V. Tarovik,et al.  Optimal ice routing of a ship with icebreaker assistance , 2019, Applied Ocean Research.

[31]  Shuhong Chai,et al.  Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms , 2019, IFAC-PapersOnLine.

[32]  H. Yasukawa,et al.  Introduction of MMG standard method for ship maneuvering predictions , 2015 .

[33]  Yamin Huang,et al.  A rule-aware time-varying conflict risk measure for MASS considering maritime practice , 2021, Reliab. Eng. Syst. Saf..

[34]  Feng Ma,et al.  A novel path planning approach for smart cargo ships based on anisotropic fast marching , 2020, Expert Syst. Appl..

[35]  Guo Hui,et al.  Optimal search path planning for unmanned surface vehicle based on an improved genetic algorithm , 2019, Comput. Electr. Eng..

[36]  Neeraj Kumar,et al.  Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges , 2020, Comput. Commun..

[37]  Wang Peng,et al.  An obstacle avoidance strategy for the wave glider based on the improved artificial potential field and collision prediction model , 2020, Ocean Engineering.