发表机构
Clemson University（克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究类鱼机器人控制和运动规划难题。开发高效模拟平台，用PID控制结合时间反向传播和课程训练学习增益，实现运动控制与路径跟踪，策略应用于物理平台效果良好。
AI 中文摘要
在过去几十年中，类鱼游动启发了数十个甚至数百个仿生机器人的设计。但由于流体 - 结构相互作用建模不佳以及此类机器人的非线性欠驱动动力学，其控制和运动规划一直具有挑战性。虽然强化学习在地面和空中机器人领域取得了显著进展，但缺乏具有适当计算速度和精度的合适模拟环境阻碍了类鱼机器人的类似进展。我们通过开发一个以计算效率近似类鱼机器人运动的模拟平台来解决这两个问题。然后使用PID控制进行机器人的运动控制和路径跟踪，其中（可变）增益通过时间反向传播学习并在课程上进行训练。在模拟中学习的策略随后应用于物理平台，显示出极佳的匹配效果。
英文摘要
Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.
CommentsAccepted to IROS 2026, 9 pages, 12 Images, 1 Table
