A motion planning method for unmanned surface vehicle in restricted waters

The maneuvering characteristics of the unmanned surface vehicle itself are very important to motion planning due to the limited water scale area. If the size, motion state, and maneuvering characteristics of the unmanned surface vehicle are not considered, the shortest path obtained is actually not feasible in the restricted waters. In this article, the widely used A* algorithm is improved by accounting for the maneuvering characteristics of the unmanned surface vehicle, named as the Label-A* Algorithm, which is further employed to fix the problem related to the motion planning for the unmanned surface vehicle in restricted waters. The solution to the motion planning mainly contains three stages. First, the unmanned surface vehicle trajectory unit library is established based on its maneuvering characteristics; second, an improved label-A* Algorithm is constructed, and the unmanned surface vehicle motion planning method is proposed with the trajectory unit, which is suitable for the restricted waters; Finally, numerical simulations and filed tests are designed to verify the formulated model and proposed algorithm. The motion planning method can simultaneously meet the state constraints, maneuvering characteristics constraints, and water scale constraints of unmanned surface vehicle.

[1]  Tian Baoqian,et al.  Analysis on Movement Efficiency for Wave Driven Unmanned Surface Vehicle , 2014 .

[2]  Yuanchang Liu,et al.  A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment , 2017 .

[3]  Tao Yang,et al.  Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments , 2018 .

[4]  A.J. Shafer,et al.  Autonomous cooperation of heterogeneous platforms for sea-based search tasks , 2008, OCEANS 2008.

[5]  Todd Litman,et al.  Autonomous Vehicle Implementation Predictions: Implications for Transport Planning , 2015 .

[6]  Yuanchang Liu,et al.  Smoothed A* algorithm for practical unmanned surface vehicle path planning , 2019, Applied Ocean Research.

[7]  Xinping Yan,et al.  Multi-objective path planning for unmanned surface vehicle with currents effects. , 2018, ISA transactions.

[8]  Satyandra K. Gupta,et al.  GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning , 2012, Robotics Auton. Syst..

[9]  Kenzo Nonami,et al.  Prospect and Recent Research & Development for Civil Use Autonomous Unmanned Aircraft as UAV and MAV , 2007 .

[10]  George W. Irwin,et al.  A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres , 2012, Annu. Rev. Control..

[11]  Robert Sutton,et al.  The design of a navigation, guidance, and control system for an unmanned surface vehicle for environmental monitoring , 2008 .

[12]  P. S. Tseng,et al.  Path planning on satellite images for unmanned surface vehicles , 2015 .

[13]  G. Gartner,et al.  Lecture Notes in Geoinformation and Cartography , 2006 .

[14]  Yuanqiao Wen,et al.  Trajectory-cell based method for the unmanned surface vehicle motion planning , 2019, Applied Ocean Research.

[15]  A. Lazarowska.  Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation , 2015 .

[16]  Oliver Bittel,et al.  Fast grid based collision avoidance for vessels using A∗ search algorithm , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

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

[18]  Satyandra K. Gupta,et al.  Generation of State Transition Models Using Simulations for Unmanned Sea Surface Vehicle Trajectory Planning , 2011 .

[19]  Yuanqiao Wen,et al.  Motion planning for Unmanned Surface Vehicle based on Trajectory Unit , 2018 .

[20]  Jianhua Wang,et al.  An Unmanned Surface Vehicle for Multi-mission Applications , 2009, 2009 International Conference on Electronic Computer Technology.

[21]  T.H Yang,et al.  Development of unmanned surface vehicle for water quality monitoring and measurement , 2018, 2018 IEEE International Conference on Applied System Invention (ICASI).

[22]  Yuanchang Liu,et al.  The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method , 2016 .

[23]  Man Zhu,et al.  Maritime Unmanned Vehicle Cruise Path Planning for Maritime Information Collection , 2016 .

[24]  Robin R. Murphy,et al.  User Interface for Unmanned Surface Vehicles Used to Rescue Drowning Victims , 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[25]  Lino Marques,et al.  Robots for Environmental Monitoring: Significant Advancements and Applications , 2012, IEEE Robotics & Automation Magazine.

[26]  Alexandre M. Amory,et al.  A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions , 2019, Sensors.

[27]  Roland Siegwart,et al.  State Estimation for Shore Monitoring Using an Autonomous Surface Vessel , 2014, ISER.

[28]  G. Oriolo,et al.  On-line map building and navigation for autonomous mobile robots , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[29]  Junku Yuh,et al.  Applications of marine robotic vehicles , 2011, Intell. Serv. Robotics.

[30]  Marco Bibuli,et al.  Basic navigation, guidance and control of an Unmanned Surface Vehicle , 2008, Auton. Robots.

[31]  Zhixiang Liu,et al.  Unmanned surface vehicles: An overview of developments and challenges , 2016, Annu. Rev. Control..

[32]  Yan Peng,et al.  Sea surface object tracking for USV with spatio-temporal context fusion , 2018 .

[33]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[34]  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.

[35]  E.T. Steimle,et al.  Unmanned Surface Vehicles as Environmental Monitoring and Assessment Tools , 2006, OCEANS 2006.

[36]  Manhar Dhanak,et al.  High-level fuzzy logic guidance system for an unmanned surface vehicle (USV) tasked to perform autonomous launch and recovery (ALR) of an autonomous underwater vehicle (AUV) , 2014, 2014 IEEE/OES Autonomous Underwater Vehicles (AUV).

[37]  Axel Hahn,et al.  Trajectory Planning with Negotiation for Maritime Collision Avoidance , 2015 .