A serial robotic manipulator arm is a complex electromechanical system whose performance is characterized by its actuators. The actuator itself is a complex nonlinear system whose performance can be characterized by the speed and torque capabilities of its motor, and its accuracy depends on the resolution of the encoder as well as its ability to resist deformations under load. The mechanical gain associated with the transmission is critical to the overall performance of the actuator since it amplifies the motor torque, thus improving the force capability of the manipulator housing it, reduces the motor speed to a suitable output speed operating range, and amplifies the stiffness improving the precision under load of the overall system. In this work, a basic analytic process that can be used to manage the actuator gain parameter to obtain an improved arm design based on a set of desired/required performance specifications will be laid out. Key to this analytic process is the mapping of the actuator parameters (speed, torque, stiffness, and encoder resolution) to their effective values at the system output via the mechanical gains of the actuators as well as the effective mechanical gains of the manipulator. This forward mapping of the actuator parameters allows the designer to determine how each of the parameters influences the functional capacity of the serial manipulator arm. The actuator gains are then distributed along the effective length of the manipulator to determine their effects on the performance capabilities of the system. The analytic formulation is also demonstrated to be effective in addressing the issue of configuration management of serial robotic manipulators where the goal is to assemble a system that meets some required performance specifications. To this end, two examples demonstrating a solution of the configuration management problem are presented. The analytic process developed based on the mapping of the mechanical parameters of the actuator to their effective values at the system output is shown to dramatically reduce the effort in the initial phases of the design process, meaning that the number of design iterations can be dramatically reduced.

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