发表机构
Johns Hopkins University Whiting School of Engineering（约翰斯·霍普金斯大学怀廷工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对间歇观测下估计器误差非零均值问题，提出规划器条件误差模型与矩递归，在滚动时域规划中惩罚预测误差，仿真显示误差预测损失降低13.4%，终端任务指标从1.60降至0.99。
AI 中文摘要
使用单独设计或现成估计器的信念空间规划器可能获取到估计器未观测到的状态相关信息。因此，即使估计器在其自身信息下最小化均方误差，当以规划器信息为条件时，其误差均值也可能非零。当未来的校正间歇且随机时，接受与拒绝的误差均值之间的差异会引入额外的校正不确定性项，而零均值假设会忽略这些项。本文提出一种信念空间规划方法，该方法沿评估轨迹预测、传播并惩罚规划器条件下的估计器误差。规划器条件估计器误差模型将仿射误差动力学与保持随机诱导校正不确定性的矩递归相结合。我们描述了一种在指定运行机制内预测估计器误差动力学的方法，并将预测的误差矩纳入任务加权二次风险目标。我们针对倾转旋翼垂直起降飞行器在舰船甲板上的降落问题，使用滚动时域规划进行仿真评估。我们发现，以规划器信息为条件，相对于忽略规划器的模型，命令盲扩展卡尔曼滤波器的误差预测损失降低了13.4%，且预测能力强烈依赖于机制和时域。在一组16对闭环试验中，规划器将终端任务指标的中位数从1.60降至0.99，并在2秒内预测估计器误差在1.3倍以内，而仅协方差规划的预测低估了5.7倍。
英文摘要
Belief-space planners with separately designed or off-the-shelf estimators may have access to state-relevant information the estimator does not observe. Consequently, even an estimator that minimizes mean-squared error under its own information can have a nonzero error mean when conditioned on planner information. When future corrections are intermittent and stochastic, differences between accepted and rejected error means introduce additional uncertainty terms to correction events that zero-mean assumptions ignore. In this paper, we propose a belief-space planning approach that predicts, propagates, and penalizes planner-conditioned estimator error along evaluated trajectories. A planner-conditioned estimator-error model combines affine error dynamics with a moment recursion that preserves stochastically-induced correction uncertainty. We describe a method by which to predict estimator error dynamics within specified operating regimes and incorporate predicted error moments into a task-weighted quadratic risk objective. We evaluate the approach in simulation for a tilt-rotor VTOL landing on a ship deck using receding-horizon planning. We find that conditioning on planner information reduced error-prediction loss for a command-blind EKF by 13.4% relative to a planner-ignorant model, with strongly regime and horizon-dependent forecasting abilities. In a small set of 16 paired closed-loop trials, the planner lowered the median terminal task gauge from 1.60 to 0.99, and predicted estimator error beyond 2s within a factor of 1.3, versus a 5.7-fold underprediction by covariance-only planning.
