Configuration Optimization and Advanced Docking Design for Two Classes of Reconfigurable Robotic 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，这源于通过使用少量基本的批量生产模块构建复杂的机器人结构来降低总体成本的潜力。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功能。此外，MSRR系统通过重新排列其模块的连接来有效地重新配置其形态的能力使该类机器人能够适应环境的变化，这使得此类系统在监视，自主探索和搜索和救援等应用中非常有吸引力。该研究计划的目标是推进MRR和MSRR的设计和应用，使这些系统在制造、勘探和军事等应用领域的更大规模集成。为此，申请人已经为申请的NSERC DG项目确定了以下研究目标，需要解决这些目标，以实现这一目标：(i)开发基于任务的配置优化（TBCO）算法用于串行型MRR：该算法将利用申请人开发的多解IK求解器来确定给定任务的最合适的MRR配置。为了提高申请人提出的MRR TBCO求解器的计算性能和速度，将开发一种新的模因算法（Memetic Algorithm， MA）作为核心优化算法。（ii）开发先进的自主对接方法和姿态优化算法，用于MSRR信息：当多个移动MSRR通过灵巧接口连接时，它们可以通过分散协作执行复杂任务。然而，为了增强MSRR的效用，特别是它的子类别，即移动配置变化（MCC）类，在与非结构化环境中的探索和导航相关的应用程序中，机动性、灵活性和在不平坦表面上导航的能力必须得到显著增强。因此，本研究的另一个关键目标是通过以下方式实现这一目标：(1)开发并实验验证了一种基于任务遗传算法的灵巧串联臂链式连接的msrs位姿优化算法。为了验证我们提出的基于任务的姿态优化算法，将利用三个移动机械手（现有于申请人的研究实验室）通过其机载机械机械手串联连接，并且(2)开发一种基于电磁/永磁男女接口的MSRR模块自主对接的新机制，该机制具有被动球面关节和扩展卡尔曼滤波（EKF）估计算法。在室内应用中，利用红外传感器和轮式编码器对两个对接移动模块的相对位置和方向进行了EKF估计。申请人研究实验室中的移动机械手将使用所提出的对接机制进行改造，并将对该过程进行实验验证。对于户外应用，该方法将通过使用摄像头和LED标记而不是红外传感来扩展。申请人有一个证明的记录，有能力的团队和最先进的实验室，以成功地完成上述研究目标，使利益相关者的研究人员和行业能够从这种模块化工程系统中获益。

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.