Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach

TL;DR

This paper introduces a novel lexicographic genetic algorithm to optimize modular robot configurations, significantly improving the search for task-specific morphologies in complex industrial environments.

Contribution

It presents a new hybrid genetic algorithm with lexicographic evaluation that effectively explores large design spaces for modular robot composition.

Findings

Outperforms existing baseline methods in robot synthesis tasks

Successfully designs modular robots for complex industrial environments

Handles larger search spaces than previous approaches

Abstract

Industrial robots are designed as general-purpose hardware with limited ability to adapt to changing task requirements or environments. Modular robots, on the other hand, offer flexibility and can be easily customized to suit diverse needs. The morphology, i.e., the form and structure of a robot, significantly impacts the primary performance metrics acquisition cost, cycle time, and energy efficiency. However, identifying an optimal module composition for a specific task remains an open problem, presenting a substantial hurdle in developing task-tailored modular robots. Previous approaches either lack adequate exploration of the design space or the possibility to adapt to complex tasks. We propose combining a genetic algorithm with a lexicographic evaluation of solution candidates to overcome this problem and navigate search spaces exceeding those in prior work by magnitudes in the number of possible compositions. We demonstrate that our approach outperforms a state-of-the-art baseline and is able to synthesize modular robots for industrial tasks in cluttered environments.