基于改进DWA的无人艇编队避障及MPC轨迹跟踪

盛金亮, 董早鹏, 匡文琪, 李志豪, 孙蓬勃

doi:10.16411/j.cnki.issn1006-7736.2024.04.002

大连海事大学学报 >

2024 , Vol. 50 >Issue 4: 12 - 21

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2024.04.002

基于改进DWA的无人艇编队避障及MPC轨迹跟踪

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(1.武汉理工大学船海与能源动力工程学院，武汉 430063；2.中国船舶集团有限公司综合技术经济研究院，北京 100081)

盛金亮（1998 — ），男，硕士生，研究方向：USV编队控制；董早鹏（1988 — ），男，博士，副教授， E-mail：dongzaopeng@whut.edu.cn。

网络出版日期: 2024-06-05

基金资助

国家自然科学基金青年基金项目（51709214）

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Obstacle avoidance and MPC trajectory tracking of unmanned surface vehicle formation based on improved DWA

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(1. School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China;2. Chinese Shipbuilding Corporation Limited Comprehensive Technical and Economic Research Institute,Beijing 100081,China)

Online published: 2024-06-05

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摘要

针对突发障碍物影响下的无人艇编队轨迹跟踪控制问题，提出一种基于改进动态窗口法的自主-跟随双模式避障模型预测控制方法。首先，为实现无人艇避障规划和控制的更好融合，在动态窗口算法评价函数设计中引入纵向速度和艏向角速度震荡约束项以减少速度和艏向角的抖动，提出一种基于中间距离计算引导方位角的策略以减少无人艇避障过程中的急速转弯情况。在跟随艇自主避障过程中，设计基于预测终点与期望位置偏差的评价项，同时，结合队形保持信息修正避障终点，以减小整个无人艇编队队形的偏离。其次，基于线性化泰勒展开原理，设计无人艇编队的预测模型，通过系统输出测量值与模型预估值之间的预测误差对模型的预测值进行修正，同时，采用滚动式有限时域的反复在线优化策略，提出一种融合自主-跟随双模式避障策略的无人艇编队轨迹跟踪模型预测控制方法。最后，设计非线性干扰观测器对环境干扰进行补偿，结合终端惩罚理论构造李雅普诺夫函数证明系统的稳定性。仿真实验验证了本文无人艇编队避障和轨迹跟踪控制算法的有效性和可靠性。

关键词： 无人艇编队; 避障规划; 轨迹跟踪; 模型预测控制; 动态窗口法

本文引用格式

盛金亮, 董早鹏, 匡文琪, 李志豪, 孙蓬勃 . 基于改进DWA的无人艇编队避障及MPC轨迹跟踪[J]. 大连海事大学学报, 2024 , 50(4) : 12 -21 . DOI: 10.16411/j.cnki.issn1006-7736.2024.04.002

Abstract

A self-following dual-mode obstacle avoidance model predictive control method based on improved dynamic window method was proposed for the trajectory tracking control problem of unmanned surface vehicle (USV) formation under the influence of sudden obstacles. Firstly, the longitudinal velocity and bow angular velocity oscillation constraints were introduced to reduce the jitter of the velocity and bow angle in the design of the evaluation function of the dynamic window algorithm so as to achieve better integration of obstacle avoidance planning and control. A strategy of calculating the steering azimuth based on intermediate distance was proposed to reduce the sharp turns in the obstacle avoidance process of the USV, while an evaluation term based on the deviation of the predicted and the expected position ending was designed. Meanwhile, the formation keeping information was combined to correct the obstacle avoidance endpoint and reduce the deviation of the USV formation. Secondly, based on the linearized Taylor expansion principle, a prediction model for USV formation was designed. The prediction value of the model was corrected by the prediction error between the system output measurement and the model prediction values. At the same time, a rolling finite time domain iterative online optimization strategy was adopted to propose a trajectory tracking model prediction control method for USV formation that integrated autonomous following dual-mode obstacle avoidance strategy. Finally, a nonlinear disturbance observer was designed to compensate the environmental disturbances, while the Lyapunov function was constructed to prove the stability of the system by combining with the terminal penalty theory. Eventually, the effectiveness and reliability of the proposed USV formation obstacle avoidance and trajectory tracking control algorithm were verified by simulation experiments.

Key words： unmanned surface vehicle (USV) formation; obstacle avoidance planning; trajectory tracking; model predictive control; dynamic window algorithm

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