The deformable linear objects (DLOs) are common in both industrial and domestic applications, such as wires, cables, ropes. Because of its highly deformable nature, it is difficult for the robot to reproduce human's dexterous skills on DLOs. In this paper, the unknown deformation model is estimated in both the offline and online manners. The offline learning aims to provide a good approximation prior to the manipulation task, while the online learning aims to compensate the errors due to insufficient training (e.g. limited datasets) in the offline phase. The offline module works by constructing a series of supervised neural networks (NNs), then the online module receives the learning results directly and further updates them with the technique of adaptive NNs. A new adaptive controller is also proposed to allow the robot to perform manipulation tasks concurrently in the online phase. The stability of the closed-loop system and the convergence of task errors are rigorously proved with Lyapunov method. Simulation studies are presented to illustrate the performance of the proposed method.
翻译:可变形线性物体(DLOs)在工业和国内应用中都很常见,例如电线、电缆、绳索等。由于其高度变形性质,机器人很难在DLOs上复制人类的发光技能。在本文中,未知变形模型是以离线和在线方式估算的。离线学习的目的是在操作任务之前提供一个良好的近似,而在线学习的目的是弥补由于离线阶段的培训不足(例如有限的数据集)而造成的错误。脱线模块通过建立一系列监管神经网络(NNS)进行工作,然后在线模块直接获得学习结果,并用适应NNNS技术进一步更新这些结果。还提议设立一个新的适应控制器,使机器人能够在在线阶段同时执行操作任务。闭环系统的稳定性和任务错误的趋同与Lyapunov方法的趋同得到了严格证明。模拟研究是为了说明拟议方法的性能。