Configuration optimization, path planning and control of serial modular and reconfigurable robots

串行模块化和可重构机器人的配置优化、路径规划和控制

• 批准号: 312069-2009
• 负责人: Melek, William
• 金额: $ 2.19万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=504925
• 关键词: Configuration; optimization; path; planning; control

Visionary Manufacturing Challenges for 2020 by the USA National Research Council points out that among all priority technology areas in manufacturing "reconfigurable and adaptable systems" are most important. There exist several manufacturing approaches aimed at increasing production flexibility, such as lean manufacturing, and just-in-time production. In the field of robotics, recent efforts exploit modular and reconfigurable robots (MRRs) for the purpose of enabling manufacturing flexibility. Moreover, in recent years, several manufacturing tools suppliers began to offer MRR-based solutions for design of versatile facilities that offer improved dexterity over task-specific hardware for automation. This need for MRRs will continue to grow in order to meet the projected demand for adaptable manufacturing systems in upcoming years. Therefore, the applicant's research program will focus on advancing motion planning and control methodologies of MRR to facilitate wider scale use of those systems in advanced manufacturing applications. First, the research program will focus on developing intelligent path planning methods to solve the Inverse Kinematics (IK) problem for MRRs. By incorporating adaptive niching methods, the proposed approach will be able to find multiple solutions of the IK problem for configurations that have no closed form analytical solution. Secondly, the program will target the development of configuration-independent robust control methods for MRRs. Finally, the research program will focus on development of configuration optimization strategies for MRRs that utilize novel GA-based task-based algorithms. The proposed algorithms will utilize the multi-solution IK solver developed by the applicant during the research program to identify the most suitable MRR configuration for a given task. Through the successful execution of the aforementioned milestones, this research program will enable the utilization of MRRs in a wide variety of manufacturing applications.

美国国家研究委员会发布的《2020 年远见制造挑战》指出，在制造业的所有优先技术领域中，“可重构和适应性系统”最为重要。存在多种旨在提高生产灵活性的制造方法，例如精益制造和准时生产。在机器人领域，最近的努力利用模块化和可重构机器人（MRR）来实现制造灵活性。此外，近年来，一些制造工具供应商开始提供基于 MRR 的解决方案，用于设计多功能设施，这些设施比特定于自动化任务的硬件提供了更高的灵活性。为了满足未来几年对适应性制造系统的预计需求，对 MRR 的需求将继续增长。因此，申请人的研究计划将侧重于推进 MRR 的运动规划和控制方法，以促进这些系统在先进制造应用中更广泛的使用。首先，该研究计划将重点开发智能路径规划方法来解决 MRR 的逆运动学 (IK) 问题。通过结合自适应小生境方法，所提出的方法将能够为没有封闭形式解析解的配置找到 IK 问题的多个解决方案。其次，该计划的目标是开发独立于配置的 MRR 鲁棒控制方法。最后，该研究计划将重点开发利用基于遗传算法的新型任务算法的 MRR 配置优化策略。所提出的算法将利用申请人在研究计划期间开发的多解决方案 IK 解算器来识别给定任务的最合适的 MRR 配置。通过成功执行上述里程碑，该研究计划将使 MRR 在各种制造应用中得到利用。