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: $ 2.91万
• 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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611967
• 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.

申请人的研究计划的主题是开发新的运动控制方法，可以有效地为具有对接能力的高实用模块化移动机器人(MMR)的前沿类别处理干扰/不确定性。在安全、探索、科学数据收集、搜索和救援等应用中，移动机器人的使用正在急剧增加。在移动机器人领域，MMR代表着一种前沿类别，与非模块化机器人相比，MMR提供了显著改善的移动性和感知性。因此，最近开发它们的能力和增强其功能的研究努力是及时的。此外，开发高效和新颖的MRR运动控制算法将使其能够在广泛的应用范围内得到应用。该方案中将要开发的运动控制方法包括MMR在动态环境中运行的路径规划和控制算法，这些动态环境中存在噪声和不确定性，而实际机器人操作环境中经常会遇到这种情况。本提案中考虑的编队要么是停靠的(刚性)，要么是灵活的。据申请人所知，前者(对接的)可以处理动力学环境中的干扰/不确定性的鲁棒运动控制方法在文献中尚未介绍。对于灵活的队形，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，这些缺点阻碍了对MMR实施有效的运动控制，特别是在存在噪声和干扰的情况下。本研究计划的重点是开发新的MRRS运动控制理论框架，但为了验证这些技术，还将进行大量的仿真和实验。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操作。所提出的方法也可以应用于其他可以完全或部分利用所开发的技术的系统，如汽车、航空航天和军事/国防。该项目旨在培训几名本科生和研究生，学习机电一体化系统的设计、建模、先进传感和控制，这些系统对自动化、采矿、航空航天和汽车等行业有很高的要求。