Structural Synthesis and Optimisation of a Robotic Gripper Using Generative AI Design

Abstract

As a problem-solving activity, engineering design is usually iterative involving multiple proposed solutions that are tested against a predefined set of constraints. Human designers usually rely on their knowledge, experience, and intuition, which is a drawback when dealing with certain unknown problems. This is easily overcome by an AI that can generate and test several thousand alternative solutions to a design problem iteratively in the form of a parametric computational model. This paper seeks to present one such automated design process involving the development and testing of a low-maintenance robotic gripper featuring underactuation and reduced weight for missions in extreme environments. This is achieved by considering the computer as a collaborative partner in the design process, where the cloud computing engines generate thousands of mechanically improved designs in response to our rigorous and robust input computational model. Generated solutions include uniquely synthesised structures designed to achieve the aforementioned objectives. Notable contributions of this paper are presented through a comparative study confirming the gripper’s improved component accessibility, structural resilience, and doubled weight-to-power ratio achieved through 73% crude weight reduction compared to its predecessor.