Synergizing Morphological Computation and Generative Design: Automatic Synthesis of Tendon-Driven Grippers

TL;DR

This paper introduces a novel automated design methodology combining morphological computation and generative design to create tendon-driven robotic grippers capable of versatile object grasping.

Contribution

It presents a new graph grammar-based approach for automatically synthesizing linkage mechanisms for robots with morphological computation.

Findings

Successfully designed a tendon-driven gripper for diverse objects

Automated the design process using graph grammar and heuristic search

Validated the approach on a simple object grasping task

Abstract

Robots' behavior and performance are determined both by hardware and software. The design process of robotic systems is a complex journey that involves multiple phases. Throughout this process, the aim is to tackle various criteria simultaneously, even though they often contradict each other. The ultimate goal is to uncover the optimal solution that resolves these conflicting factors. Generative, computation or automatic designs are the paradigms aimed at accelerating the whole design process. Within this paper we propose a design methodology to generate linkage mechanisms for robots with morphological computation. We use a graph grammar and a heuristic search algorithm to create robot mechanism graphs that are converted into simulation models for testing the design output. To verify the design methodology we have applied it to a relatively simple quasi-static problem of object grasping. We found a way to automatically design an underactuated tendon-driven gripper that can grasp a wide range of objects. This is possible because of its structure, not because of sophisticated planning or learning.