Enhanced Performance, Stability, and Practicability of Attitude and Position Estimators for Robotic Vehicles

这项建议的研究目标是进一步的国家的最先进的姿态和姿态估计机器人车辆。这项工作的动机是空中机器人车辆的激增，这些车辆目前或设想自动检查基础设施，监测建筑和采矿作业，在城市地区运送货物，在偏远地区提供医疗援助，并监测农业和野生动物，通常靠近人类。这些任务对加拿大的基础设施维护和更新、经济增长、野生动物保护和北方地区的支持都很重要。典型的空中机器人车辆具有有限的计算资源，并且其机载传感器提供不完善的数据。为了可靠、有效和安全地使用航空机器人系统，无论是单独使用还是团队使用，都需要姿态或姿态和位置（即，姿态）必须通过计算简单并且不受偏差和噪声破坏传感器数据的影响的算法来估计。直接估计描述车辆姿态的方向余弦矩阵（DCM）估计器已经得到普及，因为它们计算简单并且可证明是渐近稳定的，不像卡尔曼滤波器，例如扩展卡尔曼滤波器和无迹卡尔曼滤波器。此外，通过直接估计DCM，这是一个全球性的和唯一的姿态表示，DCM参数化的缺陷，如奇异性的避免。然而，现有技术的DCM估计器以及估计姿态和位置两者的类似姿态估计器不主动地过滤破坏内感受和外感受测量数据的偏差和噪声，分别诸如速率陀螺仪和磁力计。因此，姿态和姿态估计是穷人，这反过来，负面影响机器人车辆的精确和准确的操作。所提出的研究的总体目标和预期结果是通过消除测量偏差和噪声的不利影响，实现机器人车辆在三维空间中旋转和平移的特殊姿态和位置估计。这将通过集成一个干扰估计器来估计偏差和噪声破坏内感受测量，使用一个专门的线性时不变系统来过滤外感受测量，并使用不同的估计误差项来提高估计器的收敛性，同时保证DCM和姿态估计器的渐近稳定性。四名博士生、三名工程硕士生和五名本科生将密切参与拟议的研究。

The research objective of this proposal is to further the state-of-the-art in attitude and pose estimation for robotic vehicles. This work is motivated by the proliferation of aerial robotic vehicles that currently or are envisioned to autonomously inspect infrastructure, monitor construction and mining operations, deliver goods in urban areas and medical aid in remote regions, and monitor agriculture and wildlife, often in close proximity to humans. These tasks are important to Canada's infrastructure maintenance and replacement, economic growth, wildlife conservation, and support of Northern regions. Typical aerial robotic vehicles have limited computational resources, and their on-board sensors provide imperfect data. For reliable, effective, and safe use of aerial robotic systems, either individually or in teams, the attitude or attitude and position (i.e., pose) of the vehicle must be estimated by an algorithm that is computationally simple and immune to bias and noise corrupting sensor data. Direction cosine matrix (DCM) estimators that estimate the DCM describing a vehicle's attitude directly have gained popularity because they are computationally simple and are provably asymptotically stable, unlike Kalman-like filters such as the extended and unscented Kalman filters. Moreover, by estimating the DCM directly, which is a global and unique representation of attitude, deficiencies of DCM parameterizations such as singularities are avoided. However, state-of-the-art DCM estimators, as well as similar pose estimators that estimate both attitude and position, do not actively filter bias and noise that corrupts interoceptive and exteroceptive measurement data, such as rate gyros and magnetometers, respectively. As a result, attitude and pose estimates are poor which, in turn, negatively impacts the precise and accurate operation of robotic vehicles. The overarching goal of the proposed research, and anticipated outcome, is realizing exceptional attitude and position estimates of robotic vehicles rotating and translating in three-space by negating the detrimental impact of measurement bias and noise. This will be achieved by integrating a disturbance estimator to estimate bias and noise corrupting interoceptive measurements, using a specialized linear time-invariant system to filter exteroceptive measurements, and using a different estimation error term to improve estimator convergence, all while guaranteeing asymptotic stability of DCM and pose estimators. Four PhD students, three MEng students, and five undergraduate students will be intimately involved in the proposed research.