Global path planning for autonomous ship: A hybrid approach of Fast Marching Square and velocity obstacles methods

Abstract In this research, a hybrid approach for global path planning for Maritime Autonomous Surface Ship (MASS) is proposed, which generates the shortest path considering the collision risk and the proximity between path and obstacles. The collision risk concerning obstacles is obtained using Time-Varying Collision Risk (TCR) concept, taking into account the velocity constraint of the ship that can achieve during operation. The influence of proximity from obstacles is measured with the Fast Marching (FM) algorithm. A new cost function is proposed allowing to combine the influence of obstacle proximity and collision risk in the region. Finally, the Fast Marching Square algorithm is applied to generate the globally optimal path that can reach the pre-set destination. The contribution of this work is two-fold: 1) considering the velocity constraint of the own ship, together with its influences of collision risk into the global path planning stage of autonomous navigation. 2) measuring the collision risk induced by the obstacles from their comprehensive influences on the achievable velocity range using TCR concept, instead of numerical integration of risk measurement. The results of the case study indicate that the proposed approach can find an optimal path considering the collision risk and proximity from the obstacles.

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

[2]  Pengfei Chen,et al.  Ship collision avoidance methods: State-of-the-art , 2020 .

[3]  Yang Wang,et al.  Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes , 2020 .

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

[5]  P.H.A.J.M. van Gelder,et al.  Velocity obstacle algorithms for collision prevention at sea , 2018 .

[6]  Santiago Garrido,et al.  Fast marching subjected to a vector field-path planning method for mars rovers , 2017, Expert Syst. Appl..

[7]  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.

[8]  Hadi Ghaderi,et al.  Autonomous technologies in short sea shipping: trends, feasibility and implications , 2018, Transport Reviews.

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

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

[11]  Marco Pavone,et al.  Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions , 2013, ISRR.

[12]  Yamin Huang,et al.  Time‐Varying Risk Measurement for Ship Collision Prevention , 2019, Risk analysis : an official publication of the Society for Risk Analysis.

[13]  Agnieszka Lazarowska,et al.  A Discrete Artificial Potential Field for Ship Trajectory Planning , 2019, Journal of Navigation.

[14]  Luis Moreno,et al.  Planning robot formations with fast marching square including uncertainty conditions , 2013, Robotics Auton. Syst..

[15]  Ron Kimmel,et al.  Fast Marching Methods , 2004 .

[16]  Anastasios M. Lekkas,et al.  Hybrid Collision Avoidance for ASVs Compliant With COLREGs Rules 8 and 13–17 , 2019, Frontiers in Robotics and AI.

[17]  Jin-Suk Lee,et al.  Evaluation of ship collision risk assessments using environmental stress and collision risk models , 2019, Ocean Engineering.

[18]  Morten Breivik,et al.  The branching‐course model predictive control algorithm for maritime collision avoidance , 2019, J. Field Robotics.

[19]  Eric Kulbiej,et al.  Autonomous Vessels' Pathfinding Using Visibility Graph , 2018, 2018 Baltic Geodetic Congress (BGC Geomatics).

[20]  Pentti Kujala,et al.  Improving stand-on ship's situational awareness by estimating the intention of the give-way ship , 2020, Ocean Engineering.

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

[22]  Sanjay Sharma,et al.  Towards use of Dijkstra Algorithm for Optimal Navigation of an Unmanned Surface Vehicle in a Real-Time Marine Environment with results from Artificial Potential Field , 2018 .

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

[24]  P.H.A.J.M. van Gelder,et al.  Multi-attribute decision-making method for prioritizing maritime traffic safety influencing factors of autonomous ships’ maneuvering decisions using grey and fuzzy theories , 2019, Safety Science.

[25]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Jakub Montewka,et al.  Towards the assessment of potential impact of unmanned vessels on maritime transportation safety , 2017, Reliab. Eng. Syst. Saf..

[27]  Pengfei Chen,et al.  Ship collision candidate detection method: A velocity obstacle approach , 2018, Ocean Engineering.

[28]  Khaled Akka,et al.  Mobile robot path planning using an improved ant colony optimization , 2018 .

[29]  Shasha Wang,et al.  A Multi-Layered Potential Field Method for Water-Jet Propelled Unmanned Surface Vehicle Local Path Planning with Minimum Energy Consumption , 2019, Polish Maritime Research.

[30]  Pengfei Chen,et al.  Probabilistic risk analysis for ship-ship collision: State-of-the-art , 2019, Safety Science.

[31]  Yong Yin,et al.  Fast Path Planning for Autonomous Ships in Restricted Waters , 2018, Applied Sciences.

[32]  Luis Moreno,et al.  The Path to Efficiency: Fast Marching Method for Safer, More Efficient Mobile Robot Trajectories , 2013, IEEE Robotics & Automation Magazine.

[33]  Yong Yin,et al.  COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields , 2018, Journal of Navigation.

[34]  Yong Qi,et al.  Towards a Model of Regional Vessel Near-miss Collision Risk Assessment for Open Waters based on AIS Data , 2019, Journal of Navigation.

[35]  Santiago Garrido,et al.  Marine Applications of the Fast Marching Method , 2020, Frontiers in Robotics and AI.

[36]  Guoqing Xia,et al.  Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm , 2019, Mathematical Problems in Engineering.

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