Design and Input Allocation for Robots with Saturated Inputs via Genetic Algorithms

In this paper we consider fully-actuated and redundantly-actuated robots, whose saturated inputs can have high bandwidth or can be slowly varying (with dynamics). The slowly varying inputs can be considered as configurations for the system. The proposed strategy allows to find the optimal actuators’ configuration to optimize a cost function as the manipulability or the energy consumption. The approach allows for both a static design, which can include actuators’ parameters such as position, orientation, saturations, numbers of actuators, and for a dynamic design, where the configurations can be controlled by an input of the system. A generalized solution to the optimal problem is proposed with the use of genetic algorithms. The results are validated in two simulation scenarios: a reconfiguration of the actuators orientation of an redundantly-actuated planar robot for trajectory tracking and the design optimization of the orientation of the motors in a generalized hexa-rotor with arbitrary propeller orientation.