Robot swarms offer inherent robustness and the capacity to execute complex, collaborative tasks surpassing the capabilities of single-agent systems. Co-designing these systems is critical, as marginal improvements in individual performance or unit cost compound significantly at scale. However, under traditional frameworks, this scale renders co-design intractable due to exponentially large, non-intuitive design spaces. To address this, we propose SwarmCoDe, a novel Collaborative Co-Evolutionary Algorithm (CCEA) that utilizes dynamic speciation to automatically scale swarm heterogeneity to match task complexity. Inspired by biological signaling mechanisms for inter-species cooperation, the algorithm uses evolved genetic tags and a selectivity gene to facilitate the emergent identification of symbiotically beneficial partners without predefined species boundaries. Additionally, an evolved dominance gene dictates the relative swarm composition, decoupling the physical swarm size from the evolutionary population. We apply SwarmCoDe to simultaneously optimize task planning and hardware morphology under fabrication budgets, successfully evolving specialized swarms of up to 200 agents -- four times the size of the evolutionary population. This framework provides a scalable, computationally viable pathway for the holistic co-design of large-scale, heterogeneous robot swarms.

翻译：机器人集群具有固有的鲁棒性，并能执行超越单智能体系统能力的复杂协作任务。对这些系统的协同设计至关重要，因为个体性能或单元成本上的微小改进在大规模场景下会产生显著累积效应。然而，在传统框架下，由于设计空间呈指数级扩大且不直观，这种规模使得协同设计难以处理。为此，我们提出SwarmCoDe——一种利用动态物种形成自动扩展集群异构性以匹配任务复杂度的新型协作协同进化算法。该算法受生物界物种间合作信号机制的启发，通过进化遗传标签和选择性基因，无需预设物种边界即可促进共生互利伙伴的涌现式识别。此外，进化出的显性基因控制相对集群组成，将物理集群规模与进化种群规模解耦。我们将SwarmCoDe应用于在制造预算约束下同时优化任务规划与硬件形态，成功进化出包含多达200个智能体（为进化种群规模的4倍）的专门化集群。该框架为大规模异构机器人集群的整体协同设计提供了一条可扩展、计算可行的路径。