CAJ | 학술논문

示教学习是机器人运动技能获取的一种高效手段.当采用摄像机作为示教轨迹记录部件时,示教学习涉及如何通过反复尝试获得未知机器人摄像机模型问题.本文力图针对非线性系统重复作业中的可重复不确定性学习,提出一个迭代学习神经网络控制方案,该控制器将保证系统最大跟踪误差维持在神经网络有效近似域内.为此提出了一个适合于重复作业应用的分布式神经网络结构.该神经网络由沿期望轨线分布的一系列局部神经网络构成,每一局部神经网络对对应期望轨迹点邻域进行近似并通过重复作业完成网络训练.由于所设计的局部神经网络相互独立,因此一个全程轨迹可以通过分段训练完成,由起始段到结束段,逐段实现期望轨迹的准确跟踪.该方法在具有未知机器人摄像机模型的轨迹示教模仿中得到验证,显示了它是一种高效的训练方法,同时具有一致的误差限界能力.
시교학습시궤기인운동기능획취적일충고효수단.당채용섭상궤작위시교궤적기록부건시,시교학습섭급여하통과반복상시획득미지궤기인섭상궤모형문제.본문력도침대비선성계통중복작업중적가중복불학정성학습,제출일개질대학습신경망락공제방안,해공제기장보증계통최대근종오차유지재신경망락유효근사역내.위차제출료일개괄합우중복작업응용적분포식신경망락결구.해신경망락유연기망궤선분포적일계렬국부신경망락구성,매일국부신경망락대대응기망궤적점린역진행근사병통과중복작업완성망락훈련.유우소설계적국부신경망락상호독립,인차일개전정궤적가이통과분단훈련완성,유기시단도결속단,축단실현기망궤적적준학근종.해방법재구유미지궤기인섭상궤모형적궤적시교모방중득도험증,현시료타시일충고효적훈련방법,동시구유일치적오차한계능력.
Learning from demonstration is an efficient way for transferring movement skill from a human teacher to a robot.Using a camera as a recorder of the demonstrated movement, a learring strategy is required to acquire knowledge about the nonlinearity and uncertainty of a robot-camera system through repetitive practice. The purpose of this paper is to design a neural network controller for vision-based movement imitation by repetitive tracking and to keep the maximum training deviation from a demonstrated trajectory in a permitted region. A distributed neural network structure along a demonstrated trajectory is proposed.The local networks for a segment of the trajectory are invariant or repetitive over repeated training and are independent of the other segments. As a result, a demonstrated trajectory can be decomposed into short segments and the training of the local neural networks can be done segment-wise progressively from the starting segment to the ending one. The accurate tracking of the whole demonstrated trajectory is thus accomplished in a step-by-step or segment-by-segment manner. It is used for trajectory imitation by demonstration with an unknown robot-camera model and shows that it is effective in ensuring uniform boundedness and efficient training.