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The Most Accurate Path from Point A to Point B is Not Necessarily a Straight Line

机译:从A点到B点的最精确路径不一定是直线

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This work studies the problem of guiding a vehicle from a known initial location to a known goal location as accurately as possible, without direct observation of the goal location (such as a bearing measurement, or line-of-sight to the goal), and without direct position measurements, such as those provided by GPS. The vehicle travels in a planar environment and has an onboard inertial measurement unit and an onboard visual system to measure bearing angles to features in the environment. Taking a zigzagging path toward the goal provides better position estimation than a straight path. For a given energy budget, there is a certain path width, or amplitude, that results in the best estimation performance, and this optimal path width depends on the sensor noise parameters. A batch estimator is derived to analyze the effect of the entire time history of the vehicle trajectory on final position estimation performance. The formulation results in a linear system of equations. The path width that minimizes the condition number of the system matrix also minimizes the final position estimation error when the feature bearing measurement noise is relatively large compared to the inertial measurement noise.
机译:这项工作研究了如何在不直接观察目标位置(例如方位测量或到目标的视线)的情况下,将车辆从已知的初始位置准确地引导到已知的目标位置的问题,以及无需直接位置测量(例如GPS提供的位置测量)。车辆在平坦的环境中行驶,并具有车载惯性测量单元和车载视觉系统,用于测量与环境中的特征之间的方位角。与直线路径相比,采用锯齿形路径朝向目标可提供更好的位置估计。对于给定的能量预算,存在一定的路径宽度或幅度,这会导致最佳的估计性能,并且此最佳路径宽度取决于传感器的噪声参数。派生一个批估计器,以分析车辆轨迹的整个时间历史对最终位置估计性能的影响。该公式化形成线性方程组。当特征轴承测量噪声与惯性测量噪声相比较大时,使系统矩阵的条件数最小的路径宽度还将最终位置估计误差最小化。

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