Designing multi-agent robotic systems requires reasoning across tightly coupled decisions spanning heterogeneous domains, including robot design, fleet composition, and planning. Much effort has been devoted to isolated improvements in these domains, whereas system-level co-design considering trade-offs and task requirements remains underexplored. In this work, we present a formal and compositional framework for the task-driven co-design of heterogeneous multi-robot systems. Building on a monotone co-design theory, we introduce general abstractions of robots, fleets, planners, executors, and evaluators as interconnected design problems with well-defined interfaces that are agnostic to both implementations and tasks. This structure enables efficient joint optimization of robot design, fleet composition, and planning under task-specific performance constraints. A series of case studies demonstrates the capabilities of the framework. Various component models can be seamlessly incorporated, including new robot types, task profiles, and probabilistic sensing objectives, while non-obvious design alternatives are systematically uncovered with optimality guarantees. The results highlight the flexibility, scalability, and interpretability of the proposed approach, and illustrate how formal co-design enables principled reasoning about complex heterogeneous multi-robot systems.

翻译：设计多智能体机器人系统需要综合考虑异构领域中的紧密耦合决策，包括机器人设计、编队组成与规划。大量工作致力于这些领域的独立优化，而综合考虑权衡与任务要求的系统级协同设计仍未被充分探索。本文提出一种形式化且具有组合性的框架，用于任务驱动的异构多机器人系统协同设计。基于单调协同设计理论，我们将机器人、编队、规划器、执行器与评估器抽象为具有明确定义接口的互联设计问题，这些接口对实现和任务均无关。该结构能够在特定任务性能约束下实现机器人设计、编队组成与规划的高效联合优化。系列案例研究展示了该框架的能力：各类组件模型（包括新型机器人类型、任务剖面与概率传感目标）可无缝集成，同时系统性地揭示具有最优性保证的非直观设计方案。结果凸显了所提方法的灵活性、可扩展性与可解释性，并阐明了形式化协同设计如何实现对复杂异构多机器人系统的严谨推理。