乒乓球机器人中的学习机制研究
文献类型：学位论文
| 作者 | 黄艳龙 |
| 学位类别 | 工学博士 |
| 答辩日期 | 2013-05-24 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院自动化研究所 |
| 导师 | 谭民 ; 徐德 |
| 关键词 | 乒乓球机器人 轨迹预测 正则化算法 并行模糊学习 主动学习 综合学习 局部加权回归 物理知识 Ping-pong playing robot trajectory prediction regularization algorithm parallel fuzzy learning active learning integrated learning locally weighted regression physical knowledge |
| 其他题名 | Research on the Learning Mechanism in Ping-Pong Robotic System |
| 学位专业 | 控制理论与控制工程 |
| 中文摘要 | 乒乓球机器人系统涉及视觉测量、模式识别、物理建模和学习系统等，近年来得到了许多研究人员的关注。本文围绕乒乓球机器人系统中乒乓球的轨迹预测、击打点的选择以及任意来球的定点回球等问题，对机器人系统中的学习机制进行研究，研究结果分为以下几个部分： 一．针对乒乓球机器人系统中乒乓球的轨迹预测问题，本文提出了基于经验数据的模糊学习方法。依据模糊隶属度函数划分整个输入空间为多个子空间，并将经验数据存储在这些子空间中。考虑到实验中经验数据的递增性，本文给出了基于核函数的更新机制用来减少子空间中经验数据的存储。文中应用正则化算法归纳不同子空间上的数据子集，从而获得一系列的局部模型，这些局部模型可进行轨迹预测。局部模型的输出经由模糊加权机制进行整合，从而得到最终的预测结果。考虑到正则化算法中可能存在的病态问题，本文引入了鲁棒技术确保正则化算法的适定性。 二．针对击打点选择问题，本文提出了一种并行的模糊学习方法。在依据轨迹预测获得多个候选击打点后，应用近邻法估计不同候选点对应的球拍击打速度，进而得到球拍对应的近似加速度。由两个模糊子系统组成的并行学习系统可以计算球拍加速度对应的总体成功率，其中，两个模糊子系统可根据反馈结果进行在线的更新。文中同时给出了一个与球拍加速度和成功率有关的性能函数，用来对不同的候选点进行评估，从而获得最优的击打点。 三．针对定点回球问题，本文提出了一种带有反馈学习的融合系统。该系统包括基于局部加权回归的两个映射以及基于模糊小脑模型的主动学习，其中两个映射求解球拍的初始控制参数，主动学习求解球拍对应的调整参数。球拍的最终击打参数是初始参数和调整参数之和。针对反馈学习问题，文中给出了可以依据实际落点和期望落点之间的偏差在线调节模糊小脑模型中经验数据的学习算法。 四．本文提出了一种物理知识和经验数据学习相结合的综合方法。物理知识用来指导经验数据的学习，使得球拍能够将任意来球击打到期望的落点位置。经验数据根据模糊隶属度函数进行划分，并存储到不同的区域内。为了减少同一区域内具有相似信息的数据点的存储，文中给出了基于欧氏距离的存储机制。 最后是本文的结论以及对学习机制研究的展望。 |
| 英文摘要 | The ping-pong robotic system, which refers to the vision measurement, pattern recognition, physically modeling, machine learning and so on, has attracted much research attention in recent years. In this dissertation, the machine learning methods in the ping-pong robotic system are investigated. The main contributions of the thesis are stated as follows: Firstly, a memory-based fuzzy learning approach is proposed for trajectory prediction in the ping-pong robotic system. The experience data are stored in multiple subspaces which are obtained by dividing the input space according to the fuzzy membership functions. Due to the continuous increment of the experience data in the real game, a kernel function based update mechanism is proposed to reduce the data storage in the subspaces. The regularization algorithm is used to generalize the data subsets in different subspaces independently. Then, a series of local models for the data subsets are obtained. These local models will be used for trajectory prediction. The outputs of the local models are smoothly integrated by using a fuzzy weighted algorithm. A robust technique is introduced to ensure that the regularization algorithm is well-posed. Secondly, a parallel fuzzy learning approach is proposed for determining the hitting point where the racket attached to the ping-pong playing robot will intercept the incoming ball. A series of candidate hitting points are obtained according to the trajecotry prediction of flying ball. The nearest neighbor method is used to estimate the racket velocity for each candidate hitting point, and then the approximate acceleration of the racket is obtained. A parallel fuzzy learning system consisting of two fuzzy subsystems is used to compute the success rate for the racket acceleration. Both these subsystems will be updated online based on the feedback. A performance function of the racket acceleration and the success rate is formulated to evaluate the candidate hitting points, and then the optimal hitting point is chosen. Thirdly, an active learning approach is proposed to control the racket so that the incoming ball is returned to a desired position. Two maps that are implemented with the locally weighted regression (LWR) are used to calculate the racket’s initial parameters. The active learning approach that is based on the fuzzy cerebellar model articulation controller (CMAC) is used to calculate the racket’s adjustment parameters. The racket’s final parameters are the... |
| 语种 | 中文 |
| 其他标识符 | 201018014628005 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/6511]  |
| 专题 | 毕业生_博士学位论文 |
| 推荐引用方式 GB/T 7714 | 黄艳龙. 乒乓球机器人中的学习机制研究[D]. 中国科学院自动化研究所. 中国科学院大学. 2013. |
入库方式： OAI收割
来源：自动化研究所
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