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对机器人车的态度和姿势估计。这项工作是由空中机器人车的扩散引起的，该机器人车辆目前或设想自主检查基础设施，监测建筑和采矿业务，在城市地区提供货物以及在偏远地区提供医疗援助，并监测农业和野生动植物，通常与人类近似。这些任务对加拿大的基础设施维护和替代，经济增长，野生动植物保护以及北部地区的支持很重要。典型的航空机器人车辆的计算资源有限，其板载传感器提供了不完美的数据。对于可靠，有效且安全的空中机器人系统，无论是单独还是在团队中，必须通过计算简单且不受偏见和噪声损坏传感器数据的算法来估算车辆的态度或态度和位置（即姿势）。估算DCM描述车辆态度的DCM的方向余弦矩阵（DCM）的估计器直接获得了流行，因为它们在计算上很简单并且在渐近稳定上是渐近的稳定性，这与像卡尔曼一样的过滤器（如巨型和无味的卡尔曼过滤器）不同。此外，通过直接估算DCM，这是态度的全球和独特表示，可以避免DCM参数化（例如奇异性）的缺陷。但是，最先进的DCM估计器以及估计态度和位置的类似姿势估计器不会主动过滤偏见和噪声，这些偏见和噪声分别损坏了跨感受和外部感受性的测量数据，例如速率陀螺和磁力计。结果，态度和姿势估计很差，反过来又对机器人车的精确运行产生了负面影响。拟议的研究的总体目标以及预期的结果是通过消除测量偏见和噪声的有害影响，实现了机器人车辆旋转​​和转换的机器人车辆的出色态度和位置估计。这将通过将干扰估计器整合到估计偏差和噪声损坏的互感测量值，使用专门的线性时间不变的系统来过滤外观感受性测量值，并使用不同的估计误差项来提高估计器收敛，同时又可以确保DCM和POSE估算器的渐进性稳定性。四名博士生，三名Meng学生和五名本科生将密切参与拟议的研究。