论文标题
游戏理论实用树多机器人合作追求策略
Game-theoretic Utility Tree for Multi-Robot Cooperative Pursuit Strategy
论文作者
论文摘要
在危险场景中，多基因系统（MAS）之间的基本关系可以表示为游戏理论模型。在对抗环境中，对手可以根据他们的需求和动机是故意的或无意的。代理商将采用合适的决策策略来最大化其当前需求并最大程度地减少其预期成本。本文提出并扩展了新的基于层次网络的模型，称为游戏理论实用树（GUT），以达成一项合作追求策略，以捕捉追求逃避游戏领域的逃避者。与常规恒定轴承（CB）和Pure Pursuit（PP）策略相比，我们使用Robotarium平台验证并证明了所提出的方法的性能。实验证明了肠道的有效性，并且表演证明了肠道可以有效地组织合作策略，从而帮助小组较少的优势实现了更高的绩效。
Underlying relationships among multiagent systems (MAS) in hazardous scenarios can be represented as game-theoretic models. In adversarial environments, the adversaries can be intentional or unintentional based on their needs and motivations. Agents will adopt suitable decision-making strategies to maximize their current needs and minimize their expected costs. This paper proposes and extends the new hierarchical network-based model, termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit strategy to catch an evader in the Pursuit-Evasion game domain. We verify and demonstrate the performance of the proposed method using the Robotarium platform compared to the conventional constant bearing (CB) and pure pursuit (PP) strategies. The experiments demonstrated the effectiveness of the GUT, and the performances validated that the GUT could effectively organize cooperation strategies, helping the group with fewer advantages achieve higher performance.
