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

增强机器人车辆姿态和位置估计器的性能、稳定性和实用性

• 批准号: RGPIN-2016-04692
• 负责人: Forbes, James
• 金额: $ 2.4万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684689
• 关键词: Enhanced; Performance; Stability; Practicability; Attitude

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.

该提案的研究目标是进一步发展机器人车辆姿态和姿态估计的最先进技术。这项工作的动机是空中机器人车辆的激增，这些机器人目前或设想用于自动检查基础设施、监控建筑和采矿作业、在城市地区运送货物和在偏远地区提供医疗援助，以及监控农业和野生动物，这些通常在距离目的地较近的地方进行。人类。这些任务对于加拿大的基础设施维护和更换、经济增长、野生动物保护以及北部地区的支持都很重要。典型的空中机器人飞行器的计算资源有限，并且其机载传感器提供的数据不完善。为了可靠、有效和安全地使用空中机器人系统，无论是单独还是团队，必须通过计算简单且不受偏差和噪声破坏的算法来估计飞行器的姿态或姿态和位置（即姿态）传感器数据。直接估计描述车辆姿态的 DCM 的方向余弦矩阵 (DCM) 估计器已经受到欢迎，因为它们计算简单并且可证明渐近稳定，这与卡尔曼滤波器（例如扩展卡尔曼滤波器和无迹卡尔曼滤波器）不同。此外，通过直接估计 DCM（态度的全局且唯一的表示），避免了 DCM 参数化的缺陷，例如奇点。然而，最先进的 DCM 估计器以及估计姿态和位置的类似姿态估计器不会主动过滤会破坏内感受和外感受测量数据的偏差和噪声，例如速率陀螺仪和磁力计。因此，姿态和姿态估计很差，这反过来又对机器人车辆的精确操作产生负面影响。拟议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有害影响，实现在三空间中旋转和平移的机器人车辆的特殊姿态和位置估计。这将通过集成扰动估计器来估计偏差和噪声破坏内感受测量，使用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收敛性来实现，同时保证 DCM 的渐近稳定性和姿势估计器。四名博士生、三名硕士生和五名本科生将密切参与拟议的研究。