使用拦截与驱赶策略的空中集群防御
Aerial Swarm Defense using Interception and Herding Strategies
AI总结：
针对风险偏好与风险规避型空中攻击者集群，本文融合拦截与驱赶策略，提出基于 MIP 和几何启发式分配的多模式防御框架，并通过理论、数值与仿真验证。
摘要：
本文提出一种多模式解决方案，用于防御圆形受保护区域（目标），应对由风险偏好型和/或风险规避型攻击智能体（攻击者）集群发起的多种攻击。所提出的多模式方案结合了两种防御策略：1）拦截策略，使防御者团队拦截多个风险偏好型攻击者，同时确保防御者彼此不发生碰撞；2）驱赶策略，将风险规避型攻击者集群驱赶到安全区域。具体而言，本文开发了混合整数规划（MIP）和受几何启发的启发式方法，以便在攻击者表现出不同时空行为时，将防御者分配并指派和/或重新指派至拦截与驱赶任务；这些行为包括分裂成更小集群以更容易躲避防御者，或部分风险偏好型攻击者实施高速机动以最大化对受保护区域的破坏。本文对这些 MIP 与启发式方法的计算成本进行了理论和数值比较，并在仿真中演示了整体方法。
英文摘要：
This paper presents a multi-mode solution to the problem of defending a circular protected area (target) from a wide range of attacks by swarms of risk-taking and/or risk-averse attacking agents (attackers). The proposed multi-mode solution combines two defense strategies, namely: 1) an interception strategy for a team of defenders to intercept multiple risk-taking attackers while ensuring that the defenders do not collide with each other, 2) a herding strategy to herd a swarm of risk-averse attackers to a safe area. In particular, we develop mixed integer programs (MIPs) and geometry-inspired heuristics to distribute and assign and/or reassign the defenders to interception and herding tasks under different spatiotemporal behaviors by the attackers such as splitting into smaller swarms to evade defenders easily or high-speed maneuvers by some risk-taking attackers to maximize damage to the protected area. We provide theoretical as well as numerical comparisons of the computational costs of these MIPs and the heuristics, and demonstrate the overall approach in simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
