发表机构
New York University Abu Dhabi（纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于贝塞尔曲线参数化与优化问题设计水下航行器最优激励轨迹，在实验室水槽中验证，通过前向仿真评估辨识参数，实现可靠系统辨识。
AI 中文摘要
本文提出了一种通过设计最优激励轨迹来对水下航行器进行系统辨识的结构化方法。为此，轨迹采用贝塞尔曲线进行参数化，这确保了平滑且可微的运动轮廓，同时通过适当操控控制点便于施加约束。我们构建了一个优化问题，以确定一条动态可行的激励轨迹，该轨迹在尊重安全限制的同时最大化采集数据的质量，从而能够利用最小二乘法对航行器的动力学参数进行可靠估计。所提出的方法在实验室水槽中进行了实验验证，通过在前所未见的轨迹上进行前向仿真预测航行器速度，评估了从优化轨迹中辨识出的动力学参数。
英文摘要
In this work, we propose a structured methodology for the system identification of underwater vehicles through the design of optimal excitation trajectories. To this end, the trajectories are parameterized using Bezier curves, which ensure smooth and differentiable motion profiles while facilitating the enforcement of constraints through appropriate manipulation of the control points. An optimization problem is formulated to determine a dynamically feasible excitation trajectory that respects safety limits and maximizes the quality of the collected data, thereby enabling reliable estimation of the vehicle's dynamic parameters using least squares. The proposed methodology is experimentally validated in a laboratory water tank, where the dynamic parameters, identified from the optimized trajectory, are evaluated by predicting the vehicle's velocity through forward simulation on previously unseen trajectories.
CommentsAccepted for publication at the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026)
