Multi-objective design optimization of a soft, pneumatic robot

Abstract

We present a method for the design optimization of a soft, inflatable robot. The method described utilizes a multi-objective fitness function together with custom, platform-specific metrics related to the dexterity and load-bearing capacity of inflatable manipulators. Candidate designs are scored by computing these metrics at many randomly generated configurations and then by appropriately combining these scores within the multi-objective optimization framework. High performing designs are propagated through a genetic algorithm. The final result is a set of diverse, optimal designs lying along a Pareto front spanning the design space. By examining variations and trade-offs within this set, a designer can more appropriately choose design parameters for a target application. This is especially relevant for robots with many design parameters that can quickly be manufactured as is the case with emerging, soft robot technologies.