Task-Driven Co-Design of Heterogeneous Multi-Robot Systems

2026-04-23

AI Summary

This work addresses the tight coupling among robot design, formation control, and task planning in heterogeneous multi-robot systems, where existing approaches lack a unified framework that simultaneously accounts for task requirements and system-level trade-offs. To bridge this gap, the paper introduces a formal co-design framework grounded in monotone co-design theory, which abstracts robots, formations, and planners as interconnected modules with well-defined interfaces. This enables joint optimization under explicit task-performance constraints. The proposed framework is the first to support task-driven, composable, and scalable co-design, allowing seamless integration of new robot types, task configurations, and perception objectives while systematically discovering non-intuitive solutions with optimality guarantees. Case studies demonstrate that the approach significantly outperforms existing methods in flexibility, scalability, and interpretability.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• heterogeneous multi-robot systems
• task-driven co-design
• fleet composition
• robot design
• planning

Innovation

• task-driven co-design
• heterogeneous multi-robot systems
• monotone co-design theory
• compositional framework
• joint optimization