Configuration Optimization and Advanced Docking Design for Two Classes of Reconfigurable Robotic Systems

两类可重构机器人系统的配置优化和先进对接设计

• 批准号: RGPIN-2014-04596
• 负责人: Melek, William
• 金额: $ 1.75万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566622
• 关键词: Configuration; Optimization; Advanced; Docking; Design

Modular and Reconfigurable Robots (MRR) and Modular and Self-Reconfigurable Robots (MSRR) offer a great economic advantage stemming from the potential of lowering the overall cost by building complex robotic structures from few rudimentary mass-produced modules. MRR technology can provide the manufacturing and automotive industries with flexible solutions to lower cost of the capital equipment and provide the much needed reusability feature. Furthermore, the ability of MSRR systems to efficiently reconfigure its morphology by rearranging the connectivity of its modules enables robots in this class to adapt to changes in the environment which makes such systems very attractive in applications related to surveillance, autonomous exploration, and search and rescue. The goal of this research program is to advance the state-of-the-art in design and utilization of MRR and MSRR, enabling wider scale integration of such systems in applications areas such as manufacturing, exploration, and military. To do so, the applicant has identified the following research objectives for the NSERC DG program being applied for that need to be tackled in order to realize this goal: (i) Development of task-based configuration optimization (TBCO) algorithm for serial-type MRR: this algorithm will utilize a multi-solution IK solver developed by the applicant to identify the most suitable MRR configuration for a given task. In order to boost the computational performance and speed of the TBCO solvers for MRR proposed by the applicant, a novel Memetic Algorithm (MA) will be developed as the core optimization algorithm, (ii) Development of advanced autonomous docking methods and pose optimization algorithms for MSRR in formation: When several mobile MSRRs are linked via dexterous interfaces, they can perform complex tasks through decentralized collaboration. However, in order to enhance the utility of MSRR, and in particular its subcategory referred to Mobile Configuration Change (MCC) class in applications related to exploration and navigation in unstructured environments, maneuverability, dexterity, and ability to navigate on uneven surfaces has to be significantly enhanced. Hence, another key objective of the proposed research is make significant advances towards this goal through: (1) development and experimental validation of a task-based genetic algorithm-based pose optimization algorithm for MSRRs linked in chained formation by dexterous serial arms. To validate our proposed task-based pose optimization algorithm, a formation of three mobile manipulators (existing at the applicant’s research lab) serially connected through their on-board mechanical manipulators will be utilized, and (2) development of a novel mechanism for autonomous docking of MSRR modules based on electromagnetic/permanent magnet male-female interfaces with passive spherical joint and an Extended Kalman Filter (EKF) estimation algorithm. The EKF is proposed for estimation of the relative position and orientation of two docking mobile modules which utilize IR sensors and wheel encoders in indoor applications. Mobile manipulators in the applicant’s research lab will be retrofitted with the proposed docking mechanism and the process will be validated experimentally. For outdoor applications, the methodology will be extended by using a camera and LED markers instead of IR sensing. The applicant has a proven record, capable team, and state-of-the-art laboratory to succeed in accomplishing the above-mentioned research objectives in order to enable stakeholder researchers and industries to reap the benefits of such modular engineering systems.

模块化和可重构机器人（MRR）和模块化和自重构机器人（MSRR）提供了巨大的经济优势，这是由于通过从几个基本的大规模生产的模块构建复杂的机器人结构来降低总成本的潜力。MRR技术可以为制造业和汽车行业提供灵活的解决方案，以降低资本设备的成本，并提供急需的可重用性。此外，MSRR系统的能力，以有效地重新配置其形态，通过重新安排其模块的连接，使机器人在这一类，以适应环境的变化，这使得这样的系统非常有吸引力的应用程序有关的监视，自主探索，搜索和救援。该研究计划的目标是推进MRR和MSRR设计和利用的最新技术，使此类系统在制造，勘探和军事等应用领域实现更大规模的集成。为此，申请人已经确定了所申请的NSERC DG计划的以下研究目标，为了实现该目标，需要解决这些研究目标：（i）开发用于串联型MRR的基于任务的配置优化（TBCO）算法：该算法将利用由申请人开发的多解IK求解器来识别用于给定任务的最合适的MRR配置。为了提高申请人提出的用于MRR的TBCO求解器的计算性能和速度，将开发一种新的Memetic算法（MA）作为核心优化算法。（ii）开发用于MSRR信息的高级自主对接方法和姿态优化算法：当若干移动的MSRR经由灵巧接口链接时，它们可以通过分散协作执行复杂任务。然而，为了增强MSRR的实用性，特别是其在与非结构化环境中的探索和导航相关的应用中被称为移动的配置改变（MCC）类的子类别，机动性、灵活性和在不平坦表面上导航的能力必须被显著增强。因此，所提出的研究的另一个关键目标是通过以下方式朝着这一目标取得重大进展：（1）开发和实验验证基于任务的基于遗传算法的姿态优化算法，用于通过灵巧的串行臂以链式形式连接的MSRR。为了验证我们提出的基于任务的位姿优化算法，一个三个移动的机械手的编队，（存在于申请人的研究实验室）通过其机载机械操纵器串联连接，（2）基于电磁/永磁被动球铰阴阳接口和扩展卡尔曼滤波器（EKF）的MSRR模块自主对接新机制的开发估计算法EKF提出了估计的相对位置和方向的两个对接移动的模块，利用红外传感器和车轮编码器在室内应用。申请人的研究实验室中的移动的机械手将被改造为所提议的对接机制，并且该过程将通过实验进行验证。对于户外应用，该方法将通过使用摄像头和LED标记而不是IR传感来扩展。申请人拥有经过验证的记录，有能力的团队和最先进的实验室，能够成功实现上述研究目标，以使利益相关者研究人员和行业能够从这种模块化工程系统中获益。