Uncertainty in state or model parameters is common in robotics and typically handled by acquiring system measurements that yield information about the uncertain quantities of interest. Inputs to a nonlinear dynamical system yield outcomes that produce varying amounts of information about the underlying uncertain parameters of the system. To maximize information gained with respect to these uncertain parameters we present a Bayesian approach to data collection for system identification called Bayesian Optimal Experimental Design (BOED). The formulation uses parameterized trajectories and cubature to compute maximally informative system trajectories which obtain as much information as possible about unknown system parameters while also ensuring safety under mild assumptions. The proposed method is applicable to non-linear and non-Gaussian systems and is applied to a high-fidelity vehicle model from the literature. It is shown the proposed approach requires orders of magnitude fewer samples compared to state-of-the-art BOED algorithms from the literature while simultaneously providing safety guarantees.

翻译：机器人与系统状态或模型参数的不确定性普遍存在，通常通过获取系统测量值来获取关于不确定量的信息。非线性动力系统的输入会产生不同结果，这些结果能提供关于系统潜在不确定参数的差异化信息量。为最大化对这些不确定参数的信息获取量，我们提出一种用于系统辨识的贝叶斯数据采集方法——贝叶斯最优实验设计（BOED）。该方法利用参数化轨迹与数值积分技术计算信息量最大的系统轨迹，以在获取未知系统参数尽可能多信息的同时，在温和假设下确保系统安全性。所提方法适用于非线性和非高斯系统，并已在文献中的高保真车辆模型上得到应用。结果表明，与文献中现有最先进的BOED算法相比，本方法所需样本量低至数个数量级，同时提供安全性保障。