This work investigates an application-driven co-design problem where the motion and motors of a six degrees of freedom robotic manipulator are optimized simultaneously, and the application is characterized by a set of tasks. Unlike the state-of-the-art which selects motors from a product catalogue and performs co-design for a single task, this work designs the motor geometry as well as motion for a specific application. Contributions are made towards solving the proposed co-design problem in a computationally-efficient manner. First, a two-step process is proposed, where multiple motor designs are identified by optimizing motions and motors for multiple tasks one by one, and then are reconciled to determine the final motor design. Second, magnetic equivalent circuit modeling is exploited to establish the analytic mapping from motor design parameters to dynamic models and objective functions to facilitate the subsequent differentiable simulation. Third, a direct-collocation-based differentiable simulator of motor and robotic arm dynamics is developed to balance the computational complexity and numerical stability. Simulation verifies that higher performance for a specific application can be achieved with the multi-task method, compared to several benchmark co-design methods.

翻译：本文研究了面向应用的协同设计问题，即同时优化六自由度机器人操纵器的运动与电机，并通过一组任务来表征应用场景。与现有技术从产品目录中选择电机并针对单一任务进行协同设计不同，本文针对特定应用设计了电机几何结构及运动。为解决所提出的协同设计问题，本文在计算高效性方面做出贡献：首先，提出一个两步流程，通过逐一优化多个任务的运动与电机来识别多种电机设计方案，随后进行协调以确定最终电机设计；其次，利用磁等效电路建模建立从电机设计参数到动力学模型及目标函数的解析映射，以促进后续可微仿真；最后，开发了一种基于直接配点的电机与机械臂动力学可微仿真器，以平衡计算复杂度与数值稳定性。仿真验证表明，与多种基准协同设计方法相比，该多任务方法能够为特定应用实现更高性能。