Deliberative Trajectory Planning for Shipborne Autonomous Collision Avoidance System

Expand
  • 1. Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Marine Technology Department, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

Online published: 2020-01-06

Abstract

Based on rapidly-exploring random tree (RRT) algorithm, a deliberative trajectory planner DTP was developed to search for global trajectory between the adjacent waypoints of the long voyage. The static obstacle constraints, ship maneuvering constraints, and trajectory optimality requirements are satisfied simultaneously to ensure the feasibility, completeness and optimality in planning. Through simulations and on-water tests of a trimaran model, the proposed DTP method was demonstrated to be effective, superior and stable for the autonomous collision avoidance. The present work provides an important basis for subsequent research and future application.

Cite this article

YANG Rongwu,XU Jinsong,WANG Xin . Deliberative Trajectory Planning for Shipborne Autonomous Collision Avoidance System[J]. Journal of Shanghai Jiaotong University, 2019 , 53(12) : 1411 -1419 . DOI: 10.16183/j.cnki.jsjtu.2019.12.003

References

[1]MANKABADY S. The international maritime orga-nization, volume 1: International shipping rules[M]. London, UK: Croom Helm Ltd, 1986: 200-250. [2]European Maritime Safety Agency (EMSA). Annual overview of marine casualties and incidents[R/OL].(2017-11-13)[2018-04-11].http://www.emsa.europa.eu/emsa-documents/latest/item/3156-annual-overview-of-marine-casualties-and-incidents-2017.html. [3]DNV GL. Autonomous and remotely operated ships: DNVGL-CG-0264 [S]. Norway: DNV GL, 2018: 50-66. [4]CAMPBELL S, NAEEM W, IRWIN G W. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance mano-euvres[J]. Annual Reviews in Control, 2012, 36(2): 267-283. [5]LOE  A G. Collision avoidance for unmanned surface vehicles[D]. Trondheim, Norway: Norwegian University of Science and Technology, 2008. [6]BERTASKA I R, SHAH B, VON ELLENRIEDER K, et al. Experimental evaluation of automatically-generated behaviors for USV operations[J]. Ocean Engineering, 2015, 106: 496-514. [7]COWLAGI R V, TSIOTRAS P. Hierarchical motion planning with dynamical feasibility guarantees for mobile robotic vehicles[J]. IEEE Transactions on Robotics, 2012, 28(2): 379-395. [8]LAVALLE S M. Planning algorithms [M]. Cambridge, England, UK: Cambridge University Press, 2006: 228-237. [9]YANG R, XU J, WANG X, et al. Parallel trajectory planning for shipborne autonomous collision avoidance system[J]. Applied Ocean Research, 2019, 91: 101875. [10]GREYTAK M B, HOVER F S. Robust motion planning for marine vehicle navigation[C]//The Eighteenth International Offshore and Polar Engineering Conference. Vancouver, BC, Canada: International Society of Offshore and Polar Engineers, 2008: ISOPE-I-08-039. [11]TAN C S, SUTTON R, CHUDLEY J. Quasi-random, manoeuvre-based motion planning algorithm for autonomous underwater vehicles[DB/OL].(2015-03-02)[2018-04-11]. https://www.sciencedirect.com/science/article/pii/S147466701637971X. [12]KATSUNO K. On the maneuvering performance of a ship with the parameter of loading condition[J]. Journal of the Society of Naval Architects of Japan, 1990(168): 141-148. [13]盛振邦, 刘应中. 船舶原理[M]. 上海: 上海交通大学出版社, 2004: 279. SHENG Zhenbang, LIU Yingzhong. Ship theory[M]. Shanghai: Shanghai Jiao Tong University Press, 2004: 279. [14]FOSSEN T I, SAGATUN S I, SORENSEN A J. Identification of dynamically positioned ships[J]. Control Engineering Practice, 1996, 4(3): 369-376.
Outlines

/