This paper develops a general framework for multi-agent control synthesis, which applies to a wide range of problems with convergence guarantees, regardless of the complexity of the underlying graph topology and the explicit time dependence of the objective function. The proposed framework systematically addresses a particularly challenging problem in multi-agent systems, i.e., decentralization of entangled dynamics among different agents, and it naturally supports multi-objective robotics and real-time implementations. To demonstrate its generality and effectiveness, the framework is implemented across three experiments, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions without any approximations, which is a long-standing open problem, and safe formation navigation in dense environments.

翻译：本文提出了一种适用于多智能体控制综合的通用框架，该框架可广泛应用于具有收敛保证的各类问题，且不受底层图拓扑复杂性及目标函数显式时间依赖性的影响。所提出的框架系统性地解决了多智能体系统中一个尤为挑战性的问题，即不同智能体间纠缠动力学的分散化处理，并天然支持多目标机器人学与实时实现。为验证其通用性与有效性，该框架在三个实验中得以实施，分别是时变领导者-跟随者编队控制、无需任何近似的时变密度函数分散覆盖控制（这是一个长期存在的开放性问题），以及密集环境下的安全编队导航。