🤖 AI Summary
Existing distributed optimization frameworks lack adaptability for multi-robot collaborative tasks—including task assignment, path planning, target protection, and surveillance—due to insufficient integration of robot network topology, task semantics, and communication constraints.
Method: We propose two novel distributed optimization modeling paradigms: one tailored for constraint-coupled structures and another for aggregation-based structures. Our approach systematically unifies graph-theoretic modeling, consensus protocols, and heterogeneous communication architectures, yielding lightweight, provably convergent algorithms.
Contribution/Results: Implemented via an open-source ROS/DRT/ADMM toolchain, the framework enables embedded deployment and demonstrates decentralized, real-time coordination in both simulation and physical heterogeneous robot swarms. The open-source code supports plug-and-play cross-platform deployment, thereby addressing a critical gap in systematic research on distributed optimization for collaborative robotics.
📝 Abstract
Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection, and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss its implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots.
