Robust and Learning-Based Trajectory Planning for Maritime and Port Systems

报告人：王磊（大连理工大学）

时  间：2026年5月23日16:00

地  点：海韵园行政楼C503

内容摘要:

This presentation introduces advanced trajectory planning strategies for autonomous maritime and port systems, transitioning from robust mathematical optimization to real-time hierarchical learning frameworks. The first part addresses the challenges of parameter uncertainties (e.g., payload weight) in heavy port handling equipment. We propose a robust strategy that formholds the task as a stochastic optimal control problem. By employing Polynomial Chaos Expansion (PCE) to quantify uncertainties and an adaptive sequential convex optimization (SCvx) algorithm for efficient solving, the proposed method enhances computational efficiency by over 45% compared to advanced pseudospectral methods while ensuring high consistency with Monte Carlo simulations. The second part extends these principles to the autonomous berthing of Unmanned Surface Vehicles (USVs) in complex harbor environments. To overcome the high computational costs of online nonlinear programming, we develop a hierarchical supervised-learning framework. This approach utilizes offline expert trajectories—generated via a corridor-constrained optimization method (AGHA*+SSC)—to train a two-stage neural planner. The resulting system enables millisecond-level state-feedback control and maintains safe maneuvering even under 15% model parameter perturbations. Together, these studies demonstrate the synergy between rigorous mathematical optimization and the rapid inference capabilities of deep learning for resilient maritime operations.

个人简介：

王磊，大连理工大学数学科学学院教授，博士生导师，中国运筹学会理事、数学规划分会常务理事、辽宁省计算数学与数据智能重点实验室副主任、辽宁省数学会理事。现为大连理工大学教务处副处长兼通识与基础教育中心主任，曾任中组部第十批援疆干部担任石河子大学理学院院长(2020-23年)。目前的研究兴趣在最优控制理论、优化算法及在无人装备、航空发动机等复杂系统等工程问题中的应用。近年来，主持、参与多项国家自然科学基金项目、国家自然科学基金重大项目和企事业单位委托科技项目，先后获辽宁省教学成果展示大赛特等奖、辽宁省科学技术三等奖和辽宁省自然科学学术成果奖(学术著作类)二等奖。

联系人：李安