Leading Multi-Agent Teams to Multiple Goals While Maintaining Communication

Brian Reily, Christopher M. Reardon, Hao Zhang
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引用次数: 8

Abstract

Effective multi-agent teaming requires knowledgeable robots to have the capability of influencing their teammates. Robots are able to possess information that their human and other agent teammates do not, such as by scouting ahead in dangerous areas. To work as an effective team, robots must be able to influence their teammates when necessary and adapt to changing situations in order to move to goal positions that only they may be aware of, while remaining connected as a team. In this paper, we propose the problem of multiple robot teammates tasked with leading a multi-agent team to multiple goal positions while maintaining the ability to communicate with one another. We define utilities of making progress towards goals, maintaining communications with followers, and maintaining communications with fellow leaders. In addition, we introduce a novel regularized optimization formulation that balances these utilities and utilizes structured sparsity inducing norms to focus the leaders’ attention on specific goals and followers over time. The dynamically learned utility allows our approach to generate an action for each leader at each time step, which allows the leaders to reach goals without sacrificing communication. We show through extensive synthetic and high-fidelity simulations that our method effectively enables multiple robotic leaders to guide a multi-agent team to different goals while maintaining communication.
在保持沟通的同时,领导多代理团队实现多个目标
有效的多智能体团队要求知识渊博的机器人具有影响其队友的能力。机器人能够拥有人类和其他代理队友所不具备的信息,比如在危险地区提前侦察。作为一个有效的团队,机器人必须能够在必要时影响他们的队友,适应不断变化的情况,以便移动到只有他们可能意识到的目标位置,同时保持团队的联系。在本文中,我们提出了多个机器人队友的问题,他们的任务是带领一个多智能体团队到达多个目标位置,同时保持彼此之间的通信能力。我们定义了朝着目标前进、与追随者保持沟通以及与其他领导者保持沟通的效用。此外,我们引入了一种新的正则化优化公式来平衡这些效用,并利用结构化稀疏性诱导规范将领导者的注意力集中在特定的目标和追随者上。动态学习的实用程序允许我们的方法在每个时间步骤为每个领导者生成一个行动,这使得领导者能够在不牺牲沟通的情况下达到目标。我们通过广泛的合成和高保真仿真表明,我们的方法有效地使多个机器人领导者能够在保持沟通的同时指导多智能体团队实现不同的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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