Industrial robots are designed as general-purpose hardware, which limits their 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.

翻译：工业机器人被设计为通用硬件，这限制了其适应不断变化的任务需求或环境的能力。相比之下，模块化机器人具有灵活性，且易于定制以适配多样化需求。机器人的形态（即其形状与结构）显著影响主要性能指标：采购成本、节拍时间和能效。然而，针对特定任务确定最优模块组合仍是一个未解决的问题，这构成了开发任务定制化模块化机器人的重大障碍。现有方法要么对设计空间的探索不足，要么缺乏适应复杂任务的可能性。我们提出将遗传算法与候选解的词典式评估相结合，以克服这一问题，并在可能的组合数量上探索超出先前工作多个数量级的搜索空间。我们证明该方法优于当前最先进的基线方法，并且能够为杂乱环境中的工业任务合成模块化机器人。