Task-Driven Co-Design of Heterogeneous Multi-Robot Systems
2026-04-23 • Robotics
RoboticsMultiagent Systems
AI summaryⓘ
The authors developed a new framework to help design multi-robot systems by considering robot design, team makeup, and planning all at once instead of separately. Their method uses a formal approach that links these parts through clear interfaces, allowing them to optimize the whole system based on specific tasks. They tested their framework on several examples and showed it can handle different robot types and mission goals while finding the best overall designs. This work helps make complex robot teams easier to understand and improve through careful combined design.
multi-agent systemsrobot designfleet compositiontask planningco-designmonotone co-design theoryoptimizationheterogeneous robotsperformance constraintssystem-level design
Authors
Maximilian Stralz, Meshal Alharbi, Yujun Huang, Gioele Zardini
Abstract
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.