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满足特定任务需求，其任务性能高度依赖于机械臂的安装姿态与形态设计。因此，亟需开发能够根据任务要求与性能指标选择合适模块化机器人构型及安装姿态的方法。模块化机器人的形态变化可通过离散优化过程实现，通过选择性地添加或移除模块进行调节；而安装姿态的调整则作用在连续空间内，允许对方向与位置进行平滑且精确的改变。本研究提出了一种可同时优化模块化机械臂安装姿态与形态的计算框架。核心创新在于设计了一个在连续空间内隐式捕捉机械臂形态状态的映射函数，该变换函数将安装姿态与形态的优化统一在连续空间内。此外，优化框架整合了多项性能指标，包括最小关节驱动力、最大可操作度，并考虑了轨迹执行误差及物理安全约束。为凸显本方法的优势，我们将该问题与传统组合优化方法进行对比，并以CONCERT模块化机器人平台执行钻孔任务为例，验证了本方法在模块化机器人构型选择中的实用性。