Abstract
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.
Abstract (translated)
模块可重构操纵器允许快速适应和多样性以应对不同的应用环境，并专门满足任务的特定要求。任务性能很大程度上取决于操纵器安装的姿态和形态设计，因此需要方法来选择合适的模块化机器人配置和安装姿势来满足特定任务要求和性能需求。通过离散优化过程，可以获得模块化机器人的形态变化。相反，安装姿势的调整在连续空间中进行，允许在方向和位置上进行平滑和精确的修改。本工作介绍了一个计算框架，同时优化模块化操纵器的安装姿势和形态。工作的核心是我们设计了一个映射函数，隐含地捕捉了连续空间中操纵器的形态状态。这个变换函数将安装姿势和形态的优化在连续空间中统一起来。此外，我们的优化框架包括一系列性能度量，如最小关节努力和最大可操作性，以及轨迹执行误差和物理和安全性考虑。为了突出我们方法的优点，我们将它与之前的方法进行了比较，这些方法将类似问题视为组合优化问题，并展示了其在选择使用CONCERT模块化机器人平台执行钻井任务时的实用性。
URL
https://arxiv.org/abs/2405.01923
