Modular reconfigurable manipulators enable quick adaptation and versatility to address different application environments and tailor to the specific requirements of the tasks. Task performance significantly depends on the manipulator's mounted pose and morphology design, therefore posing the need of methodologies for selecting suitable modular robot configurations and mounted pose that can address the specific task requirements and required performance. Morphological changes in modular robots can be derived through a discrete optimization process involving the selective addition or removal of modules. In contrast, the adjustment of the mounted pose operates within a continuous space, allowing for smooth and precise alterations in both orientation and position. This work introduces a computational framework that simultaneously optimizes modular manipulators' mounted pose and morphology. The core of the work is that we design a mapping function that \textit{implicitly} captures the morphological state of manipulators in the continuous space. This transformation function unifies the optimization of mounted pose and morphology within a continuous space. Furthermore, our optimization framework incorporates a array of performance metrics, such as minimum joint effort and maximum manipulability, and considerations for trajectory execution error and physical and safety constraints. To highlight our method's benefits, we compare it with previous methods that framed such problem as a combinatorial optimization problem and demonstrate its practicality in selecting the modular robot configuration for executing a drilling task with the CONCERT modular robotic platform.

翻译：模块化可重构机械臂能够快速适应不同应用环境，并根据任务特定需求进行定制，具备高度的灵活性与通用性。任务执行性能在很大程度上取决于机械臂的安装姿态与构型设计，因此需要开发能够针对具体任务需求与性能要求，选择合适的模块化机器人配置与安装姿态的方法论。模块化机器人的形态变化可通过涉及模块选择性添加或移除的离散优化过程实现。相比之下，安装姿态的调整则在连续空间内进行，允许在朝向和位置上实现平滑且精确的改变。本文提出了一种同步优化模块化机械臂安装姿态与构型的计算框架。本工作的核心在于设计了一个映射函数，该函数能够*隐式地*在连续空间中捕捉机械臂的形态状态。此变换函数将安装姿态与构型的优化统一于连续空间内。此外，我们的优化框架整合了一系列性能指标，如最小关节力矩与最大可操作度，并考虑了轨迹执行误差以及物理与安全约束。为凸显本方法的优势，我们将其与此前将该问题视为组合优化问题的方法进行了对比，并在CONCERT模块化机器人平台上，通过执行钻孔任务验证了其在选择模块化机器人配置方面的实用性。