Robust Velocity Control for Minimum Steady State Uncertainty in Persistent Monitoring Applications
Robust Velocity Control for Minimum Steady State Uncertainty in Persistent Monitoring Applications
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DOI：
10.23919/acc.2019.8814376
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
M. Ostertag;Nikolay A. Atanasov;Tajana Simunic
中科院分区：
文献类型：
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作者：
M. Ostertag;Nikolay A. Atanasov;Tajana Simunic
我们提出了一个速度控制器的持续监测应用程序，最大限度地减少卡尔曼滤波协方差的最大特征值的任何初始传感位置和任何初始协方差。环境中的一组兴趣点可以由自主的、移动的机器人感测平台沿着沿着封闭的静态路径测量。我们在感兴趣的点作为一个维纳过程，估计由卡尔曼滤波器的环境现象建模。我们提出了一种贪婪击倒算法来确定每个周期每个感兴趣点的最佳观测数量，并将该问题表示为具有一组鲁棒性约束的线性规划。在仿真中，所提出的控制器相比，恒速和现有的一阶速度控制器在文献中。所提出的方法优于现有的方法在测试用例与一系列不同的参数：感兴趣的点的数量，观测模型的噪声水平，和最大速度。
We present a velocity controller for persistent monitoring applications that minimizes the maximum eigenvalue of the Kalman filter covariance for any initial sensing position and any initial covariance. A set of points of interest in the environment can be measured along a closed static path by an autonomous, mobile robotic sensing platform. We model the environmental phenomenon at the points of interest as a Wiener process that is estimated by a Kalman filter. We propose a Greedy Knockdown Algorithm to determine the optimal number of observations for each point of interest per cycle and formulate the problem as a linear program with a set of robustness constraints. In simulation, the proposed controller is compared to constant velocity and existing first-order velocity controllers in the literature. The proposed method outperforms existing methods across test cases with a range of different parameters: number of points of interest, noise level of the observation model, and maximum velocity.