面向运河环境自主导航的带避障约束的非线性模型预测控制
Nonlinear Model Predictive Control with Obstacle Avoidance Constraints for Autonomous Navigation in a Canal Environment
AI总结：
针对运河狭窄环境下小型巡航船的自主导航需求，提出带避障约束的非线性模型预测控制方法，通过系统辨识适配船舶动力学，结合LiDAR点云构建避障约束，经浦项运河实地实验验证了有效性。
摘要：
本文阐述了在运河环境中作业的小型巡航船自主导航能力的开发过程，并展示了在韩国浦项运河开展的实地实验结果。我们采用非线性模型预测控制（NMPC），实现运河狭窄航道内巡航船的在线轨迹规划与跟踪控制。为考虑船舶动力学的非线性特性，我们利用加速-减速、Z形机动等多种测试操纵的实验数据完成了系统辨识。为高效表征运河环境中的障碍物结构，我们将船载LiDAR传感器采集的点云数据中的运河壁参数化为线段，并将其作为避障约束。所提方法集成在单个NMPC层中，其实际性能通过在浦项运河的实验运行得到了验证。
英文摘要：
In this paper, we describe the development process of autonomous navigation capabilities of a small cruise boat operating in a canal environment and present the results of a field experiment conducted in the Pohang Canal, South Korea. Nonlinear model predictive control (NMPC) was used for the online trajectory planning and tracking control of the cruise boat in a narrow passage in the canal. To consider the nonlinear characteristics of boat dynamics, system identification was performed using experimental data from various test maneuvers, such as acceleration-deceleration and zigzag trials. To efficiently represent the obstacle structures in the canal environment, we parameterized the canal walls as line segments with point cloud data, captured by an onboard LiDAR sensor, and considered them as constraints for obstacle avoidance. The proposed method was implemented in a single NMPC layer, and its real-world performance was verified through experimental runs in the Pohang Canal.
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