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题名: AUV环境建模及行为优化方法研究
其他题名: Research on Environment Modeling and Behavior optimization of AUV
作者: 程大军
导师: 刘开周
分类号: TP242
关键词: 自主水下机器人 ; 无色卡尔曼滤波 ; 联合估计 ; 混合整数线性规划 ; 路径规划
索取号: TP242/C54/2011
学位专业: 模式识别与智能系统
学位类别: 硕士
答辩日期: 2011-05-27
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 水下机器人技术研究室
中文摘要: 随着自主水下机器人(AUV, Autonomous Underwater Vehicle)技术的不断发展,AUV的使命越来越复杂,而且很多使命要在复杂动态环境下完成,这就对AUV的自主能力提出了更高的要求。而实时环境建模和行为优化则是实现AUV自主能力的两种关键技术。本文在现有研究的基础上,首先讨论了AUV自身行为环境的建模问题,以AUV的推进器故障为例,采用了基于无色卡尔曼滤波(UKF, Unscented Kalman Filter)及其相关的改进算法对非线性系统的实时状态和参数进行联合估计;然后讨论了AUV行为优化问题,以复杂未知环境下的实时路径规划为例,采用基于混合整数线性规划(MILP, Mixed-Integer Linear Programming)的算法,实时规划出AUV的期望优化行为,控制AUV的航行。 本文的主要研究内容如下: 1.AUV自身行为环境建模问题主要研究了AUV推进器故障的建模,采用基于UKF的非线性系统实时状态和参数联合估计方法。在联合估计中,将系统中的时变参数与其真实状态联合,组成增广状态向量,再利用UKF对该增广的状态进行估计,从而得到状态和参数的估计值。数值仿真实验和半物理仿真平台验证结果表明了算法的有效性。 2.针对AUV状态和参数的联合估计,采用UKF会存在一些如算法的稳定性、实时性以及估计精度等问题,故研究采用改进的UKF算法,即自适应UKF算法和平方根UKF算法,对AUV的状态和参数进行联合估计。这里采用的基于卡尔曼滤波(KF, Kalman Filter)估计的自适应UKF算法,它是由两个并行的主、辅滤波器构成,辅助滤波器利用KF估计系统噪声方差,主滤波器利用该噪声方差的估计值,进一步预测估计系统的状态和方差。仿真表明在系统的噪声特性未知时,自适应UKF算法能够自动地调节自身参数,以弥补由于先验知识不足而产生的估计误差。本文研究的平方根UKF算法是将状态方差矩阵的平方根直接用于传递和更新,既减小了计算量,又保证了算法的稳定性。最后,比较了采用标准UKF算法、自适应UKF算法和平方根UKF算法来进行AUV状态和参数联合估计的优缺点。 3.针对AUV复杂动态环境下的实时局部路径规划的问题,本文采用了在全局坐标系下基于混合整数线性规划的最优轨迹产生方法。在全局坐标系下,把复杂动态环境下AUV路径规划这一非线性问题,描述成满足一组线性约束同时使目标函数极小的线性规划问题,嵌入基于MILP的规划器,从而得到一条满足性能要求的最优路径。该方法充分考虑了障碍物、目标、本体动力学以及传感器的约束,并结合实际AUV动力学模型进行优化,仿真实验结果验证了该方法的合理有效性。
英文摘要: With the development of the technique, the missions of AUV (Autonomous Underwater Vehicles) are becoming more and more complex, some of them must be completed in dynamic complex environment, so higher ability of autonomy is needed for the AUV. Online modeling and behavioral optimization are two key technologies to achieve autonomy of AUV. This paper, based on former research, firstly, the modeling of AUV behavior environment is discussed, take the fault of thruster for example, the UKF and related improved algorithms are used for online joint state and parameter estimation for the nonlinear system. Secondly, the behavioral optimization of AUV is discussed, take the real-time path planning in dynamic complex environment for example, the desired behavior is planned out by the MILP, which can control the navigation of AUV. This paper is arranged as the following: Firstly, modeling for behavior environment of AUV is discussed, take propulsion system as an example, then the UKF used for online joint state and parameter estimation system. In joint estimation, the time-varying parameter vector and the true state vector are concatenated into an augmented state. The UKF is employed to estimate the augmented state. Both the numerical simulation and the semi-physical simulation results are given to show the effectiveness of this approach. Secondly, the UKF, which is used for online joint state and parameter estimation, has some problems in stability, instantaneity and estimated accuracy, so two related improved UKF algorithms named adaptive UKF and square-root UKF, are proposed to improve the UKF performance. The KF-based adaptive UKF is composed of two parallel master-slave filters. The slave filter employs KF to estimate the noise covariance and the master UKF estimates the state using the current noise covariance. The simulations indicate that when unknown the statistical characteristic of noise, the adaptive UKF algorithm can automatically tune their parameters to compensate the estimate errors resulting from the lack of a priori knowledge. In the square-root UKF, the square-root matrix of state variance is propagated and updated, which can reduce the computation, and the stability of the algorithm is also guaranteed. At last, the three algorithms are compared. Finally, A MILP (Mixed Integer Linear Programming) based optimal trajectory generation method in the globe coordinate system is presented for real-time path planning of AUV in the dynamic complex environment. In the globe coordinate system, the path planning of AUV in dynamic environment is described as minimizing an objective function subjecting to a set of linear inequalities which can be easily embedded into MILP path planner. The constrains of obstacle, target, AUV dynamics, and sensor ranger are fully considered. Combining with actual dynamic model, the simulation results are given to show the effectiveness of this approach.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9261
Appears in Collections:水下机器人研究室_学位论文

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Recommended Citation:
程大军.AUV环境建模及行为优化方法研究.[硕士学位论文].中国科学院沈阳自动化研究所.2011
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