Online optimal active sensing control
Online optimal active sensing control
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DOI：
10.1109/icra.2017.7989083
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
P. Salaris;R. Spica;P. Giordano;P. Rives
中科院分区：
文献类型：
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作者：
P. Salaris;R. Spica;P. Giordano;P. Rives
研究非线性微分平坦系统的主动传感控制问题。我们的目标是通过确定系统的输入，最大限度地提高在一个时间范围内的输出所收集的信息量，以提高观测器的估计精度。特别是，我们使用的可观察性Gramian（OG）来量化所获得的信息的丰富性。首先，我们定义了一个轨迹的平坦输出的系统，通过使用B样条曲线。然后，我们利用在线梯度下降策略来移动B样条的控制点，以便在整个规划范围内积极地最大化OG的最小特征值。当系统沿着其规划（优化）轨迹运行时，使用扩展卡尔曼滤波器（EKF）来估计系统状态。为了保持对过去获取的传感器数据的记忆以用于在线重新规划，还在过去估计的状态轨迹上计算OG。然后，这是用于在线重新规划的最佳轨迹在机器人运动过程中，不断完善，通过利用EKF获得的状态估计。为了证明我们的方法的有效性，我们考虑一个简单但重要的情况下，一个平面机器人与一个单一的范围测量。仿真结果表明，沿着最优路径，EKF收敛速度更快，并提供了一个更准确的估计比沿着其他可能的（非最优）路径。
This paper deals with the problem of active sensing control for nonlinear differentially flat systems. The objective is to improve the estimation accuracy of an observer by determining the inputs of the system that maximise the amount of information gathered by the outputs over a time horizon. In particular, we use the Observability Gramian (OG) to quantify the richness of the acquired information. First, we define a trajectory for the flat outputs of the system by using B-Spline curves. Then, we exploit an online gradient descent strategy to move the control points of the B-Spline in order to actively maximise the smallest eigenvalue of the OG over the whole planning horizon. While the system travels along its planned (optimized) trajectory, an Extended Kalman Filter (EKF) is used to estimate the system state. In order to keep memory of the past acquired sensory data for online re-planning, the OG is also computed on the past estimated state trajectories. This is then used for an online replanning of the optimal trajectory during the robot motion which is continuously refined by exploiting the state estimation obtained by the EKF. In order to show the effectiveness of our method we consider a simple but significant case of a planar robot with a single range measurement. The simulation results show that, along the optimal path, the EKF converges faster and provides a more accurate estimate than along other possible (non-optimal) paths.