Robust co-design of robots via cascaded optimisation

Abstract

Optimising mechanical, control and actuator design variables together as a co-design problem enables identifying novel and better-performing robot architectures. Typically, solving such problems using conventional optimisation methods yields a single, point-based solution. Deviating from the computed optima may be necessary to ensure physical feasibility, typically associated with a performance loss. In this work, we present a two-step cascaded optimisation approach to identify non-intuitive designs and recover the loss in performance by constructing a solution space. The solution space provides robustness in the form of permissible ranges of design variable values and enables the selection of a physically feasible design. In our study, we observe (1) up to 20% of the lost performance is recovered and (2) an improvement of 30 % on the task metric in comparison to an existing robot and (3) designs with cost savings of up to 10% can be identified.