Mixed-integer flow formulations for motion planning and decision-making of networked multi-agent systems

2026-09-21

AI Summary

本文研究了在网络多智能体系统中使用基于流的连通性维护约束来解决轨迹规划和决策问题，通过混合整数线性规划方法，相比现有技术减少了不等式约束和二进制变量的数量。

Abstract

This work investigates the use of flow-based connectivity maintenance constraints in mixed-integer linear programming (MILP) trajectory planning and decision-making models for networked multi-agent systems (MAS). We integrate flow-based encodings for standard and k-hop connectivity into MILP multi-vehicle maneuvering models that are widely used alongside receding horizon planning strategies. Their necessity and sufficiency is demonstrated, guaranteeing full coverage of potential network topologies. The flow formulation for standard connectivity decreases the growth of the required inequality constraints from exponential to polynomial w.r.t. the size of the MAS when compared to the state-of-the-art subtour elimination (SEC) method. The flow-based k-hop connectivity constraints decrease the number of required binary variables and decouple its growth from the number of hops. However, the impact of these formulations in performance is not straightforward due to the introduction of a substantial number of continuous flow optimization variables and, in the case of k-hop connectivity, additional inequality constraints. We investigate this trade-off through a statistical evaluation of costs and optimization times using a conventional branch-and-bound commercial solver and trials performed with randomized environments for increasingly larger MAS. The results show that the flow formulation outperforms SEC in standard connectivity problems, enabling the solutions to be computed for larger MAS considering the imposed optimization time limit. The reduction in number of binary variables enabled by the k-hop flow formulations decreases the theoretical worst-case number of iterations required by the branch-and-bound algorithm to compute the global optimal solution. Our results show that this advantage did not translate into improvements in the average performance when compared to the baseline.

Problem

• mixed-integer linear programming
• multi-agent systems
• connectivity maintenance
• trajectory planning
• decision-making

Innovation

• flow-based connectivity
• mixed-integer linear programming (MILP)
• multi-agent systems (MAS)
• k-hop connectivity
• subtour elimination (SEC)