Optimizing Modular Robot Composition: A Lexicographic Genetic Algorithm Approach
This addresses the challenge of developing flexible, task-specific modular robots for industrial applications, representing an incremental improvement over prior methods.
The paper tackles the problem of identifying optimal module compositions for task-tailored modular robots, proposing a genetic algorithm with lexicographic evaluation that outperforms a state-of-the-art baseline and synthesizes robots for industrial tasks in cluttered environments.
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.