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.

翻译：工业机器人作为通用硬件设计，适应不断变化的任务需求或环境的能力有限。相比之下，模块化机器人提供了灵活性，可轻松定制以满足多样化需求。机器人的形态（即形状与结构）显著影响主要性能指标：采购成本、周期时间和能源效率。然而，针对特定任务确定最优模块组合仍是一个开放性问题，这阻碍了任务定制化模块化机器人的发展。以往的方法要么缺乏对设计空间的充分探索，要么无法适应复杂任务。我们提出将遗传算法与解候选的词典式评估相结合，以克服此问题，并导航搜索空间（其可能组合数量相较先前工作呈数量级增长）。实验证明，我们的方法优于现有基线方法，并能够在杂乱环境中综合出适用于工业任务的模块化机器人。