RI-Small: Adaptive Parameter and State Estimation on Groups with Application to Sensor-Based Control

这项研究旨在开发自适应方法，使基于传感器的系统能够从自己的感官体验中学习，以提高感知精度和灵活性。 我们将研究基于传感器的参数估计和状态估计的问题，特别是集中在刚体变换组。这些组结构的问题自然出现在三维感测（例如，范围扫描仪，如激光扫描仪和多波束测深声纳;多普勒声纳;和基于视觉的状态估计）和动态状态估计（例如，基于图像的状态估计;基于视觉的控制;和多自由度车辆导航和控制）。对于未知参数集或状态具有群结构的常见问题，目前很少有辨识技术。这与用于参数和状态估计的各种公知技术（即最小二乘和自适应）形成对比，其中未知参数或状态线性地出现在设备方程中并且未知是线性向量空间的元素。 预期的影响将是机器人系统能够自适应学习，以提高其导航和传感精度。 我们将这些方法应用于实际的现实世界中出现的问题，在水下航行器的传感和控制。

This research seeks to develop adaptive methods that will enable sensor-based systems to learn from their own sensory experience in order to improve their perceptual precision and dexterity. We will investigate the problem of sensor-based parameter estimation and state estimation on groups, with particular focus on the group of rigid body transformations. These group-structured problems naturally arise in three-dimensional sensing (e.g. range scanners such as laser scanners and multi-beam bathymetric sonars; Doppler sonar; and vision based state estimation) and dynamic state estimation (e.g. image-based state estimation; vision based control; and multi-degree-of-freedom vehicle navigation and control). Few identification techniques presently exist for the common problem in which the unknown parameter set or state possesses group structure. This is in contrast to the variety of well-known techniques (i.e. least-squares and adaptive) for parameter and state estimation for the case in which the unknown parameter or state appears linearly in the plant equations and the unknown is an element of a linear vector space. The anticipated impact will be robotic systems capable of adaptively learning to improve their navigation and sensing accuracy. We will apply these approaches to actual real-world problems arising in underwater vehicle sensing and control.