Autonomous Navigation Algorithm for Underactuated Unmanned Surface Vehicle Based on Model Predictive Control

doi:10.1007/s12204-023-2674-z

Abstract

Abstract: To achieve the track following and collision avoidance of underactuated unmanned surface vehicle (USV), autonomous navigation model based on model predictive control is established by including the track offset, speed variation and rule compliance as the evaluation functions and including the ship domain of dynamic/ static navigation obstacles and the mechanical characteristics limitation as constraints. The effectiveness of the model for autonomous navigation of USV in the situation of multi-ship encounters and in the complex waters with both dynamic and static obstructions is verified by several groups of simulation work. The simulation results show that the proposed model can realize the autonomous navigation of the underactuated USV under the complex waters.

Key words: underactuated unmanned surface vehicle (USV), model predictive control, track offset, speed variation, rule compliance

摘要： 为了实现欠驱动无人水面船舶(USV)的航迹跟踪和避碰功能，构建了基于模型预测控制的自主导航模型，其中包括航迹偏移、速度变化和规则遵从性作为评估函数，以及动态/静态导航障碍的船舶领域和机械特性限制作为约束条件。通过多组仿真实验验证了该模型在多船遭遇情况下以及存在动态和静态障碍物的复杂水域中对USV自主导航的有效性。仿真结果表明，所提出的模型能够在复杂水域下实现欠驱动USV自主导航。

关键词: 欠驱动无人水面船舶，模型预测控制，航迹偏移量，航速变化量，规则符合度

CLC Number:

U675

CHEN Guoquan, LI Yuqin, HUANG Zike, YANG Shenhua. Autonomous Navigation Algorithm for Underactuated Unmanned Surface Vehicle Based on Model Predictive Control[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1255-1264.

References

[1] XU H T, HINOSTROZA M A, GUEDES SOARES C. Modified vector field path-following control system for an underactuated autonomous surface ship model in the presence of static obstacles [J]. Journal of Marine Science and Engineering, 2021, 9(6): 652.

[2] XIA G Q, HAN Z W, ZHAO B. Local path planning for USV based on improved quantum particle swarm optimization [C]//2019 Chinese Automation Congress. Hangzhou: IEEE, 2019: 714-719.

[3] REN J A, ZHANG J, CUI Y N. Autonomous obstacle avoidance algorithm for unmanned surface vehicles based on an improved velocity obstacle method [J]. ISPRS International Journal of Geo-Information, 2021, 10(9): 618.

[4] XU X L, PAN W, HUANG Y B, et al. Dynamic collision avoidance algorithm for unmanned surface vehicles via layered artificial potential field with collision cone [J]. Journal of Navigation, 2020, 73(6): 1306-1325.

[5] CHEN Y L, BAI G Q, ZHAN Y, et al. Path planning and obstacle avoiding of the USV based on improved ACO-APF hybrid algorithm with adaptive early-warning [J]. IEEE Access, 2021, 9: 40728-40742.

[6] LYU H G, YIN Y. COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields [J]. Journal of Navigation, 2019, 72(3): 588-608.

[7] SHEN H Q, HASHIMOTO H, MATSUDA A, et al. Automatic collision avoidance of multiple ships based on deep Q-learning [J]. Applied Ocean Research, 2019, 86: 268-288.

[8] XIE S, GAROFANO V, CHU X, et al. Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks [J]. Ocean Engineering, 2019, 193: 106609.

[9] CHEN Z, ZHANG Y M, ZHANG Y G, et al. A hybrid path planning algorithm for unmanned surface vehicles in complex environment with dynamic obstacles [J]. IEEE Access, 2019, 7: 126439-126449.

[10] ERIKSEN B O H, BREIVIK M, PETTERSEN K Y, et al. A modified dynamic window algorithm for horizontal collision avoidance for AUVs [C]//2016 IEEE Conference on Control Applications. Buenos Aires: IEEE, 2016: 499-506.

[11] JOHANSEN T A, PEREZ T, CRISTOFARO A. Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment [J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(12): 3407-3422.

[12] HAGEN I B. Collision avoidance for ASVs using model predictive control [D]. Trondheim: Norwegian University of Science and Technology, 2017.

[13] ERIKSEN B O H, BITAR G, BREIVIK M, et al. Hybrid collision avoidance for ASVs compliant with COLREGs rules 8 and 13–17 [J]. Frontiers in Robotics and AI, 2020, 7: 11.

[14] SUN X J, WANG G F, FAN Y S, et al. Collision avoidance using finite control set model predictive control for unmanned surface vehicle [J]. Applied Sciences, 2018, 8(6): 926.

[15] YASUKAWA H, YOSHIMURA Y. Introduction of MMG standard method for ship maneuvering predictions [J]. Journal of Marine Science and Technology, 2015, 20(1): 37-52.

[16] CHEN Z J. Ship path tracking control based on model predictive control method [D]. Dalian: Dalian Maritime University, 2020 (in Chinese).

[17] BLENDERMANN W. Parameter identification of wind loads on ships [J]. Journal of Wind Engineering and Industrial Aerodynamics, 1994, 51(3): 339-351.

[18] NIENHUIS I U. Simulations of low frequency motions of dynamically positioned offshore structures [J]. Royal Institution of Naval Architects Transactions, 1987, 129: 1-19.

[19] LEE J H. Modeling and identification for nonlinear model predictive control: Requirements, current status and future research needs[M]//Nonlinear model predictive control. Basel: Birkhäuser, 2000: 269-293.

[20] XIE H W, ZHANG Y J, XING S W, et al. A method for ship autonomous collision avoidance based on model predictive control [J]. Ship Engineering, 2021, 43(8): 23-28 (in Chinese).