In industrial context, admittance control represents an important scheme in programming robots for interaction tasks with their environments. Those robots usually implement high-gain disturbance rejection on joint-level and hide direct access to the actuators behind velocity or position controlled interfaces. Using wrist force-torque sensors to add compliance to these systems, force-resolved control laws must map the control signals from Cartesian space to joint motion. Although forward dynamics algorithms would perfectly fit to that task description, their application to Cartesian robot control is not well researched. This paper proposes a general concept of virtual forward dynamics models for Cartesian robot control and investigates how the forward mapping behaves in comparison to well-established alternatives. Through decreasing the virtual system's link masses in comparison to the end effector, the virtual system becomes linear in the operational space dynamics. Experiments focus on stability and manipulability, particularly in singular configurations. Our results show that through this trick, forward dynamics can combine both benefits of the Jacobian inverse and the Jacobian transpose and, in this regard, outperforms the Damped Least Squares method.

中文翻译：

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笛卡尔机器人控制的虚拟前向动力学模型

在工业环境中，导纳控制代表了机器人与环境交互任务的一个重要方案。这些机器人通常在关节级别实现高增益干扰抑制，并隐藏对速度或位置控制接口后面的执行器的直接访问。使用腕力扭矩传感器来增加这些系统的顺应性，力分辨控制律必须将控制信号从笛卡尔空间映射到关节运动。尽管前向动力学算法非常适合该任务描述，但它们在笛卡尔机器人控制中的应用还没有得到很好的研究。本文提出了笛卡尔机器人控制的虚拟前向动力学模型的一般概念，并研究了前向映射与完善的替代方案相比如何表现。通过与末端执行器相比减少虚拟系统的链接质量，虚拟系统在操作空间动力学中变得线性。实验侧重于稳定性和可操作性，尤其是在单一配置中。我们的结果表明，通过这个技巧，前向动力学可以结合雅可比逆和雅可比转置的优点，并且在这方面优于阻尼最小二乘法。