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This work investigates an application-driven co-design problem where themotion and motors of a six degrees of freedom robotic manipulator are optimizedsimultaneously, and the application is characterized by a set of tasks. Unlikethe state-of-the-art which selects motors from a product catalogue and performsco-design for a single task, this work designs the motor geometry as well asmotion for a specific application. Contributions are made towards solving theproposed co-design problem in a computationally-efficient manner. First, atwo-step process is proposed, where multiple motor designs are identified byoptimizing motions and motors for multiple tasks one by one, and then arereconciled to determine the final motor design. Second, magnetic equivalentcircuit modeling is exploited to establish the analytic mapping from motordesign parameters to dynamic models and objective functions to facilitate thesubsequent differentiable simulation. Third, a direct-collocation-baseddifferentiable simulator of motor and robotic arm dynamics is developed tobalance the computational complexity and numerical stability. Simulationverifies that higher performance for a specific application can be achievedwith the multi-task method, compared to several benchmark co-design methods.

Application-Oriented Co-Design of Motors and Motions for a 6DOF Robot Manipulator