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地面移动机器人自主环境建模与适应控制方法研究
Alternative TitleResearch on Autonomous Environment Modeling and Adaptive Control Methods of Ground Mobile Robot
周波1,2
Department机器人学研究室
Thesis Advisor韩建达
ClassificationTP242
Keyword移动机器人 集员估计 环境建模 轨迹跟踪 点镇定
Call NumberTP242/Z73/2009
Pages133页
Degree Discipline模式识别与智能系统
Degree Name博士
2008-11-27
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract随着移动机器人应用范围的日益扩展,在动态、非结构环境下提高自主行为能力已经成为移动机器人研究领域的首要问题。本文以“863”高技术计划资助项目“复合机构移动机器人构型在线优化及控制共性技术研究”为依托,以沈阳自动化研究所自主研发的“模块化便携式履带式移动机器人”为实验平台,针对实时非线性在线估计共性方法及其在移动机器人行为环境的自主建模及环境适应运动控制等方面的引用,展开深入研究,旨在提高移动机器人对动态、非结构环境的适应能力。本论文的主要内容如下:首先,研究了二维环境下移动机器人的滑动效应建模问题。将滑动效应表达为三个时变的滑动参数,建立起带滑动参数的移动机器人运动学和动力学模型,探讨了运动模型的能控性和能观性,并结合动力学分析对侧滑参数的深层机制进行了分析。本研究内容为后续估计和控制问题的验证提供了仿真对象。其次,介绍了四种非线性在线估计共性方法,即基于线性化理论的EKF估计方法、基于无色变换的UKF估计方法、基于UKF重要性采样的UPF估计方法和基于未知但有界噪声假设的ESMF估计方法,建立了具有一般性的移动机器人在线建模结构;针对上述四种估计方法在移动机器人在线建模方面的应用进行了分析和比较研究,重点强调了保边界集员估计方法独有的优势。第三,针对ESMF估计方法本身存在的数值稳定性差、时间复杂度高以及滤波器参数难于选择的缺点,提出了基于UD分解的自适应扩展集员估计方法,将包络矩阵UD分解、观测序列更新和选择更新、滤波器参数的次优自适应选择三种策略结合起来,以提高ESMF的实时性和鲁棒性,针对滑动参数估计的仿真结果表明了所提方法的有效性。第四,针对两类带有参数不确定性的移动机器人控制问题,提出了在线估计与控制相结合的方法。其一是带未知时变滑动参数的移动机器人跟踪控制问题,采用非线性估计方法对未知参数进行在线估计,并结合动态反馈线性化和PD控制律两种控制策略,以达到全局指数跟踪的收敛结果。其二是带滑动参数和几何参数等混合不确定性的移动机器人点镇定控制问题,采用state scaling和back-stepping方法,对参数未知但有界的情形获得了全局指数收敛的点镇定结果。最后,对三维情况下移动机器人周边地形环境的在线建模问题进行了研究。采用数字高程网格地图表达地形环境,介绍了基于高斯和模型的地形估计方法。针对高斯和模型本身近似条件所引起的应用困难和精度较差的缺点,提出了基于区间集员估计理论的地形环境模型估计方法,避免了高斯和估计方法中存在的大量近似条件,改善了地形估计性能,并可获得地形的保证边界估计信息,为机器人的运动控制和构型调整提供必需的先验知识。仿真和实验研究均证明了集员地形估计方法相对于高斯和地形估计方法的优越性。
Other AbstractAutonomy is one of the most critical issues for mobile robot maneuvering in dynamic and unstructured environment, which are the necessary performance demanded by real applications. The relative control techniques are therefore, attracting more and more researches currently. In this dissertation, some of the enabling techniques for the autonomy of mobile robots, including nonlinear estimation algorithm and its integration in active modeling and control, are extensively investigated, simulated and tested with respect to the potable ground mobile robot (GMR) developed by the State Key Laboratory of Robotics. Also the author would like to acknowledge the National Hi-tech Research and Development (863) Program of China, who provides the financial support to the relative researches. First, the slipping effect of mobile robots in 2D environment is studied. Slipping coefficients are proposed and integrated into both the kinematics and dynamics of a GMR. The controllability and observability of the kinematics are analyzed, and the lateral slipping parameter is further studied in the dynamics. Without losing the generality, this research provides a model suitable to the theoretical analyses and simulation verification of the nonlinear estimation and control algorithms to be presented in the following chapters. Second, four different nonlinear estimation algorithms potentially suitable to mobile robot applications, which are the Extended Kalman Filter (EKF) based on linearization, the Unscented Kalman Filter (UKF) based on unscented transformation, the Unscented Particle Filter (UPF) based on UKF importance sampling, and the Extended Set-Membership Filter (ESMF) based on unknown but bounded noise assumption, are introduced and extensively compared by the simulations on the GMR with slipping coefficients. The advantages and disadvantages are demonstrated and concluded, while the guaranteed bound performance of ESMF is emphasized for the purpose of active modeling and control integration. This study provides reasonable motivations for the modification on the normal ESMF to be proposed in the coming chapter.  Third, a UD factorized adaptive ESMF (AESMF) is proposed with respect to the disadvantages existed in normal ESMF, such as numerical instability, high computation complexity as well as the difficulty in filter parameters selection. The new filter uses UD factorization, combined with a new sequential and selective measurement update strategy, as well as an adaptive selection scheme of the filter parameters, to improve the real-time performance and robustness of the original algorithm. Applications to slipping parameters estimation show the efficiency and improvements of the proposed method. Fourth, the active estimation algorithm is integrated into control schemes focusing on two motion control problems of mobile robots. One is the tracking problem of mobile robots with unknown time-varying slipping parameters. With nonlinear filter to estimate the unknown parameters online, dynamic feedback linearization method combined with PD control is proposed to achieve globally exponential tracking performance. The other is the point stabilization problem of mobile robots with mixed uncertainties of slipping and geometry parameters. The state scaling strategy is integrated into back-stepping scheme in order to achieve globally exponential point stabilization for unknown but bounded parameters, which can be obtained by the proposed AESMF. Finally, the online terrain modeling problem of mobile robots in 3D environment is studied. With the digital elevation grid map description, the terrain estimation method based on Gaussian sum model is introduced. To solve the difficulty caused by approximations and improve the accuracy, a new interval set-membership based terrain modeling method is proposed to reject existing disadvantages in the original algorithm, realize the guaranteed bound estimation, and provide prior knowledge required by motion control and configuration adoption of mobile robots. Simulations and experimental results show the superiority of the set-membership based method to the normal Gaussian sum based method. 
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/420
Collection机器人学研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院研究生院
Recommended Citation
GB/T 7714
周波. 地面移动机器人自主环境建模与适应控制方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2008.
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