基于控制障碍函数的欠驱动无人水下航行器椭圆障碍物避障制导

doi:10.12382/bgxb.2024.0404

摘要/Abstract

摘要：

为保证无人水下航行器在执行海岸线巡逻、大型船只避碰以及穿越密集岛礁等复杂任务时的安全性,提出一种基于控制障碍函数的椭圆模型障碍物避障方法。在对障碍物进行椭圆形建模的基础上设计包含艏向角约束的控制障碍函数,构建带有约束条件的二次规划问题,通过与无障碍环境下的制导律相结合,得到闭式解形式的避障制导律。仿真结果验证了所提方法的有效性,可全局上满足安全性和稳定性要求,对于无人水下航行器在复杂海洋环境中的安全航行具有实际应用价值。

关键词: 欠驱动无人水下航行器, 控制障碍函数, 椭圆障碍物, 避障, 制导律, 二次规划

Abstract:

In order to ensure the safety of unmanned underwater vehicle (UUV) when performing the complex tasks such as coastline patrol, collision avoidance of large vessels, and traversing dense islands and reefs, an elliptical modeling obstacle avoidance method based on control barrier function (CBF) is proposed. A control barrier function containing the heading angle constraints is designed on the basis of elliptical modeling obstacles, a quadratic programming (QP) problem with constraints is constructed, and a closed-form obstacle avoidance guidance law is proposed by combining with the guidance law in obstacle-free environment. The simulated results verify the effectiveness of the proposed method, which globally satisfies the safety and stability requirements. The proposed method has practical application value for the safe navigation of unmanned underwater vehicle in complex marine environment.

Key words: underactuated unmanned underwater vehicle, control barrier function, elliptical obstacle, obstacle avoidance, guidance law, quadratic programming

中图分类号:

野汶博, 方洋旺, 洪瑞阳, 胡祁东. 基于控制障碍函数的欠驱动无人水下航行器椭圆障碍物避障制导[J]. 兵工学报, 2025, 46(5): 240404-.

YE Wenbo, FANG Yangwang, HONG Ruiyang, HU Qidong. Elliptical Obstacle Avoidance Guidance for Underactuated Unmanned Underwater Vehicle Based on Control Barrier Function[J]. Acta Armamentarii, 2025, 46(5): 240404-.

图/表 17

图1 障碍物椭圆形建模示意图

Fig.1 Elliptical modeling of obstacles

图2 制导目标示意图

Fig.2 Guidance objective

图3 坐标系及障碍物椭圆模型

Fig.3 Coordinate system and elliptical modeling of obstacles

图4 t时刻切线角示意图

Fig.4 Tangent angle at time t

图5 避障轨迹对比

Fig.5 Comparison of obstacle avoidance trajectories

图6 不同参数的制导输入对比

Fig.6 Comparison of guidance inputs with different parameters

图7 避障约束h(x)对比

Fig.7 Comparison of obstacle avoidance constraints h(x)

图8 避障轨迹对比

Fig.8 Comparison of obstacle avoidance trajectories

图9 多障碍物避障轨迹

Fig.9 Obstacle avoidance trajectory for multiple obstacles

图10 多障碍物避障制导输入

Fig.10 Guidance input for multiple obstacles

图11 多障碍物避障约束

Fig.11 Obstacle avoidance constraints for multiple obstacles

图12 不同参数的避障轨迹

Fig.12 Obstacle avoidance trajectories with different parameters

图13 不同参数的避障制导输入

Fig.13 Guidance inputs with different parameters

图14 不同参数的避障约束h(x)

Fig.14 Obstacle avoidance constraints with different parameters

图15 不同避障方法的轨迹对比

Fig.15 Comparison of trajectories for different obstacle avoidance methods

图16 不同避障方法的制导输入u对比

Fig.16 Comparison of guidance inputs u for different obstacle avoidance methods

图17 不同避障方法的制导输入r对比

Fig.17 Comparison of guidance inputs r for different obstacle avoidance methods

参考文献 33

[1]	SAHOO A, DWIVEDY S K, ROBI P S. Advancements in the field of autonomous underwater vehicle[J]. Ocean Engineering, 2019, 181:145-160. doi: 10.1016/j.oceaneng.2019.04.011
[2]	邱志明, 孟祥尧, 马焱, 等. 海上无人系统发展及关键技术研究[J]. 中国工程科学, 2023, 25(3): 74-83. doi: 10.15302/J-SSCAE-2023.03.005
	QIU Z M, MENG X Y, MA Y, et al. Development and key technologies of maritime unmanned systems[J]. Strategic Study of CAE, 2023, 25(3): 74-83. (in Chinese) doi: 10.15302/J-SSCAE-2023.03.005
[3]	徐同乐, 刘方, 肖玉杰, 等. 国外无人反水雷装备及技术发展[J]. 兵工学报, 2022, 43(增刊2): 64-70.
	XU T L, LIU F, XIAO Y J, et al. Foreign unmanned equipment and technology development in mine countermeasures[J]. Acta Armamentarii, 2022, 43(S2): 64-70. (in Chinese) doi: 10.12382/bgxb.2022.B019
[4]	CHENG C X, SHA Q X, HE B, et al. Path planning and obstacle avoidance for AUV: a review[J]. Ocean Engineering, 2021, 235: 109355.
[5]	WANG C, CHENG C S, YANG D Y, et al. Path planning in localization uncertaining environment based on dijkstra method[J]. Frontiers in Neurorobotics, 2022, 16: 821991.
[6]	LI M C, ZHANG H J. AUV 3D path planning based on A^* algorithm[C]// Proceedings of 2020 Chinese Automation Congress. Shanghai, China: IEEE, 2020: 11-16.
[7]	HAO K, ZHAO J L, LI Z S, et al. Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm[J]. Ocean Engineering, 2022, 263(4): 112421.
[8]	WANG X W, YAO X L, ZHANG L. Path planning under constraints and path following control of autonomous underwater vehicle with dynamical uncertainties and wave disturbances[J]. Journal of Intelligent & Robotic Systems, 2020, 99(3/4): 891-908.
[9]	FANG Z R, WANG J J, DU J, et al. Stochastic optimization-aided energy-efficient information collection in internet of underwater things networks[J]. IEEE Internet of Things Journal, 2022, 9(3): 1775-1789.
[10]	郭银景, 刘琦, 鲍建康, 等. 基于人工势场法的AUV避障算法研究综述[J]. 计算机工程与应用, 2020, 56(4): 16-23. doi: 10.3778/j.issn.1002-8331.1910-0464
	GUO Y J, LIU Q, BAO J K, et al. Overview of AUV obstacle avoidance algorithm based on artificial potential field method[J]. Computer Engineering and Applications, 2020, 56(4): 16-23. (in Chinese) doi: 10.3778/j.issn.1002-8331.1910-0464
[11]	潘无为, 姜大鹏, 庞永杰, 等. 人工势场和虚拟结构相结合的多水下机器人编队控制[J]. 兵工学报, 2017, 38(2): 326-334. doi: 10.3969/j.issn.1000-1093.2017.02.017
	PAN W W, JIANG D P, PANG Y J, et al. A multi-AUV formation algorithm combining artificial potential field and virtual structure[J]. Acta Armamentarii, 2017, 38(2): 326-334. (in Chinese)
[12]	PENG Z H, GU N, ZHANG Y, et al. Path-guided time-varying formation control with collision avoidance and connectivity preservation of under-actuated autonomous surface vehicles subject to unknown input gains[J]. Ocean Engineering, 2019, 191: 106501.
[13]	CHU Z Z, WANG F L, LEI T J, et al. Path planning based on deep reinforcement learning for autonomous underwater vehicles under ocean current disturbance[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(1): 108-120.
[14]	KUWATA Y, WOLF M T, ZARZHITSKY D, et al. Safe maritime autonomous navigation with COLREGS, using velocity obstacles[J]. IEEE Journal of Oceanic Engineering, 2014, 39(1): 110-119.
[15]	ROMDLONY M Z, JAYAWARDHANA B. Stabilization with guaranteed safety using control Lyapunov-barrier function[J]. Automatica, 2016, 66: 39-47.
[16]	KOLATHAYA S, AMES A D. Input-to-state safety with control barrier functions[J]. IEEE Control Systems Letters, 2019, 3(1): 108-113.
[17]	HOU Y K, WANG H, WEI Y H, et al. Robust adaptive finite-time tracking control for intervention-AUV with input saturation and output constraints using high-order control barrier function[J]. Ocean Engineering, 2023, 268: 113219.
[18]	GLOTFELTER P, CORTÉS J, EGERSTEDT M. Nonsmooth barrier functions with applications to multi-robot systems[J]. IEEE Control Systems Letters, 2017, 1(2): 310-315.
[19]	XU Y P, LIU L, PENG Z H, et al. Safe motion planning for goal reaching of multiple autonomous surface vehicles based on high-order control barrier functions[C]// Proceedings of the 2022 IEEE 17th International Conference on Control & Automation. Naples, Italy: IEEE, 2022: 660-664.
[20]	AMES A D, XU X R, GRIZZLE J W, et al. Control barrier function based quadratic programs for safety critical systems[J]. IEEE Transactions on Automatic Control, 2017, 62(8): 3861-3876.
[21]	XIAO W, BELTA C. High-order control barrier functions[J]. IEEE Transactions on Automatic Control, 2022, 67(7): 3655-3662.
[22]	GAO S N, PENG Z H, WANG H L, et al. Safety-critical model-free control for multi-target tracking of USVs with collision avoidance[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(7): 1323-1326.
[23]	XU Y P, LIU L, GU N, et al. Multi-ASV collision avoidance for point-to-point transitions based on heading-constrained control barrier functions with experiment[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(6): 1494-1497.
[24]	CHEN Y X, SINGLETARY A, AMES A D. Guaranteed obstacle avoidance for multi-robot operations with limited actuation: a control barrier function approach[J]. IEEE Control Systems Letters, 2021, 5(1): 127-132.
[25]	徐彦平. 基于控制障碍函数的无人艇集群制导及实验验证[D]. 大连: 大连海事大学, 2023.
	XU Y P. Guidance of multiple autonomous surface vehicles based on control barrier functions with experimental validation[D]. Dalian: Dalian Maritime University, 2023. (in Chinese)
[26]	WANG J C, SHAN Q H, LI T S, et al. Collision-free formation-containment tracking of multi-USV systems with constrained velocity and driving force[J]. Journal of Marine Science and Engineering, 2024, 12(2): 304.
[27]	NISHIMOTO K, FUNADA R, IBUKI T, et al. Collision avoidance for elliptical agents with control barrier function utilizing supporting lines[C]// Proceedings of 2022 American Control Conference. Atlanta, GA, US: IEEE, 2022: 5147-5153.
[28]	DU Z, LI W F, SHI G Y. Multi-USV collaborative obstacle avoidance based on improved velocity obstacle method[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2024, 10(1): 4023049.
[29]	ZHANG P F, DING Y P, DU S X. Obstacle avoidance control for autonomous surface vehicles using elliptical obstacle model based on barrier Lyapunov function and model predictive control[J]. Journal of Marine Science and Engineering, 2024, 12(6): 1035.
[30]	KHALIL H K. Nonlinear systems[M]. 3rd edition. Upper Saddle RiverNJ, US: Prentice Hall, 2002.
[31]	高剑. 无人水下航行器自适应非线性控制技术[M]. 西安: 西北工业大学出版社, 2016.
	GAO J. Adaptive nonlinear control technology for unmanned underwater vehicles[M]. Xi’an: Northwestern Polytechnical University Press, 2016. (in Chinese)
[32]	CAO J, SUN Y S, ZHANG G C, et al. Target tracking control of underactuated autonomous underwater vehicle based on adaptive nonsingular terminal sliding mode control[J]. International Journal of Advanced Robotic Systems, 2020, 17(2): 1729881420919941.
[33]	杜宏宝, 王正杰, 唐礼喜, 等. 基于控制障碍函数的飞行器避障与制导控制[J]. 兵工学报, 2023, 44(9): 2814-2823. doi: 10.12382/bgxb.2022.1002
	DU H B, WANG Z J, TANG L X, et al. Control barrier function-based control for aircraft avoidance and guidance with dynamic obstacles[J]. Acta Armamentarii, 2023, 44(9): 2814-2823. (in Chinese) doi: 10.12382/bgxb.2022.1002