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

美国国家研究委员会的《2020年远景制造挑战》指出，在制造业的所有优先技术领域中，“可重构和可适应的系统”是最重要的。存在几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新配置的机器人(MRR)来实现制造灵活性。此外，近年来，几家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，这些设施提供了比特定任务硬件更高的灵活性，以实现自动化。这种对MRR的需求将继续增长，以满足未来几年对适应性制造系统的预测需求。因此，申请人的研究计划将专注于推进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的更大规模使用。首先，研究计划将专注于开发智能路径规划方法来解决MRR的反向运动学(IK)问题。通过引入自适应小生境方法，对于没有闭合形式解析解的配置，所提出的方法将能够找到IK问题的多个解。其次，该计划将致力于开发与配置无关的MRR鲁棒控制方法。最后，研究计划将重点开发配置优化策略的MRR，利用新的基于遗传算法的基于任务的算法。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解算器来确定针对给定任务的最合适的MRR配置。通过上述里程碑的成功执行，这项研究计划将使MRR在广泛的制造应用中得到利用。

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.