基于系统辨识的自主水面船从仿真到真实的强化学习
Sim-to-Real RL for ASVs using SysID
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中文总结 AI 辅助
该研究针对自主水面船（ASV）仿真到真实迁移的挑战，提出基于系统辨识的ASV模拟器及配套流程，仅用CAD模型和少量公开水域轨迹即可训练策略，在BlueBoat ASV上实现了路径跟踪等任务的零样本仿真到真实迁移。
中文摘要 AI 辅助
在动态海洋环境中作业的自主水面船（ASV），其路径跟踪、定点保持等任务需要鲁棒的控制策略，这使得强化学习（RL）成为经典控制器的有潜力替代方案。然而，现有ASV模拟器很少支持RL训练所需的并行环境，这类模拟器需从计算流体动力学求解器或拖曳水池试验获取准确的流体动力学模型，以设定流体动力学参数来解决仿真到真实的差距问题。为应对这些挑战，本文提出一种ASV模拟器及配套流程，可从未知的车辆动力学开始训练策略。该框架仅使用CAD模型和少量公开水域现场轨迹，来近似并优化流体动力学及推进器参数。在BlueBoat ASV上的实际部署表明，在无先验流体动力学和螺旋桨信息的情况下，可在路径跟踪和定点保持任务中实现成功的零样本从仿真到真实迁移。
英文摘要
Autonomous Surface Vehicles (ASVs) operating in dynamic marine environments require robust control policies for tasks such as path following and station keeping, making reinforcement learning (RL) a promising alternative to classical controllers. However, existing ASV simulators rarely support parallel environments for RL training. Such existing simulators require accurate hydrodynamic modeling from computational fluid dynamics solvers or towing tank tests for setting hydrodynamic parameters to address the sim-to-real gap. To address these challenges, we present an ASV simulator and accompanying pipeline that enables training policies starting from unknown vehicle dynamics. Our framework uses only a CAD model and brief set of open-water field trajectories for approximating and refining both hydrodynamic and thruster parameters. Real-world deployments on a BlueBoat ASV demonstrate successful zero-shot sim-to-real transfer in path following and station-keeping tasks without prior hydrodynamic and propeller information.
发表机构
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
