Novel Robust Motion Control Methods for the Formation of Robots with Docking Capabilities

用于形成具有对接能力的机器人的新型鲁棒运动控制方法

• 批准号: RGPIN-2014-06000
• 负责人: Biglarbegian, Mohammad
• 金额: $ 2.19万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708301
• 关键词: Novel; Robust; Motion; Control; Methods

The theme of the applicant's research program is on the development of novel motion control methodologies that can effectively handle disturbance/uncertainties for a frontier class of a high-utility modular mobile robots (MMRs) with docking capabilities. The use of mobile robots is dramatically increasing for applications such as in security, exploration, scientific data collection, search and rescue, etc. In the field of mobile robotics, MMRs represent a leading-edge class that offer significantly improved mobility and sensing compared to non-modular ones. Hence, recent research efforts to exploit their capabilities and enhance their functionalities are timely. Furthermore, developing efficient and novel motion control algorithms for MRRs will enable their utility for a wide spectrum of applications. The motion control methods to be developed in this proposal consist of both path planning and control algorithms for MMRs in formation operating in dynamic environments with the presence of noise and uncertainties, commonly encountered in actual robot operation environments. The formations considered in this proposal are either docked (rigid) or flexible. To the best of the applicant's knowledge, robust motion control methods for the former (docked) that can handle disturbances/uncertainties in dynamics environments have not been introduced in the literature. For flexible formations, limited useful research has been introduced, yet more research needs to be undertaken to overcome many shortcomings that preclude the implementation of efficient motion controls for MMRs especially with the presence of noise, disturbances. The focus of this research program is on developing novel theoretical framework for motion control of MRRs, but to verify those techniques extensive simulations and experiments will be also conducted. The developed algorithms will be implemented using advanced processors such as reconfigurable computing to further increase their real-time operations. The proposed methodologies can be also applied to other systems which can fully or partially utilize the developed techniques such as in automotive, aerospace, and military/defense. This program aims to train several undergraduate and graduate students on design, modeling, advanced sensing and control of mechatronics systems that are of high demands for several industries such as automation, mining, aerospace, and automotive.

申请人的研究项目的主题是开发新的运动控制方法，可以有效地处理具有对接能力的前沿类高实用模块化移动机器人（MMRs）的干扰/不确定性。移动机器人在安全、勘探、科学数据收集、搜索和救援等方面的应用正在急剧增加。在移动机器人领域，mmr代表了一个领先的类别，与非模块化的相比，它提供了显着改善的移动性和传感。因此，最近开发它们的能力和增强它们的功能的研究工作是及时的。此外，为mrr开发高效和新颖的运动控制算法将使其能够广泛应用。本文发展的运动控制方法包括mmr在实际操作环境中常见的存在噪声和不确定性的动态环境中运行的路径规划和控制算法。在这个建议中考虑的阵型要么是固定的（刚性的），要么是灵活的。据申请人所知，文献中尚未介绍可以处理动态环境中的干扰/不确定性的前者（停靠）的鲁棒运动控制方法。对于灵活的地层，已经引入了有限的有用研究，但需要进行更多的研究来克服许多缺点，这些缺点阻碍了mmr有效运动控制的实施，特别是在存在噪声和干扰的情况下。本研究计划的重点是为核磁共振运动控制开发新的理论框架，但为了验证这些技术，还将进行广泛的模拟和实验。开发的算法将使用可重构计算等先进处理器来实现，以进一步提高其实时性。所提出的方法也可以应用于其他系统，这些系统可以完全或部分地利用已开发的技术，例如汽车，航空航天和军事/国防。本专业旨在培养在自动化、采矿、航空航天和汽车等行业对机电一体化系统的设计、建模、高级传感和控制等方面具有较高要求的本科生和研究生。