Path Planning Method Based on D* lite Algorithm for Unmanned Surface Vehicles in Complex Environments

In recent decades, path planning for unmanned surface vehicles (USVs) in complex environments, such as harbours and coastlines, has become an important concern. The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles. In this study, we developed a novel path planning method based on the D* lite algorithm for real-time path planning of USVs in complex environments. The proposed method has the following advantages: (1) the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles; (2) a constrained artificial potential field method is employed to enhance the safety of the planned paths; and (3) the method is practical in terms of vehicle performance. The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms. The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.

[1]  Giuseppe Casalino,et al.  Towards the use of a team of USVs for civilian harbour protection: The problem of intercepting detected menaces , 2010, OCEANS'10 IEEE SYDNEY.

[2]  Sanjay Sharma,et al.  A Two Layered Optimal Approach towards Cooperative Motion Planning of Unmanned Surface Vehicles in a Constrained Maritime Environment , 2018 .

[3]  Junwei Wu,et al.  Online planning for relative optimal and safe paths for USVs using a dual sampling domain reduction-based RRT* method , 2020, Int. J. Mach. Learn. Cybern..

[4]  A. Lifei Song,et al.  A two-level dynamic obstacle avoidance algorithm for unmanned surface vehicles , 2018, Ocean Engineering.

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

[6]  Yuanchang Liu,et al.  Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment , 2015 .

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

[8]  Sven Koenig,et al.  Fast replanning for navigation in unknown terrain , 2005, IEEE Transactions on Robotics.

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

[10]  J. Majohr,et al.  Modelling, simulation and control of an autonomous surface marine vehicle for surveying applications Measuring Dolphin MESSIN , 2006 .

[11]  Richard Bucknall,et al.  Path-planning algorithm for ships in close-range encounters , 2010 .

[12]  Soh Chin Yun,et al.  Enhanced D∗ Lite Algorithm for mobile robot navigation , 2010, 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA).

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

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

[15]  Marco Bibuli,et al.  A Novel Double Layered Hybrid Multi-Robot Framework for Guidance and Navigation of Unmanned Surface Vehicles in a Practical Maritime Environment , 2020, Journal of Marine Science and Engineering.

[16]  Lei Wan,et al.  Fast marching square method based intelligent navigation of the unmanned surface vehicle swarm in restricted waters , 2020, Applied Ocean Research.

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

[18]  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.

[19]  Richard Bucknall,et al.  Cooperative path planning algorithm for marine surface vessels , 2013 .

[20]  Yong-Hoon Cho,et al.  Experimental validation of a velocity obstacle based collision avoidance algorithm for unmanned surface vehicles , 2019, IFAC-PapersOnLine.