使用几何嵌入的多智能体系统中的涌现结构
Emergent Structure in Multi-agent Systems Using Geometric Embeddings
AI总结：
提出一种去中心化控制系统架构，通过单射虚拟嵌入使多智能体仅凭局部观测自组织为全局稳定的闭合轨迹，并在四旋翼无人机集群上验证其有效性与均匀间距保持能力。
摘要：
本研究探讨了多智能体系统自组织为闭合轨迹的过程，这是无人机（UAV）监视任务中的常见要求。在此类场景中，平滑且无偏的控制信号能够节省能源并减轻机械应变。我们提出了一种去中心化控制系统架构，仅通过局部观测即可产生全局稳定的涌现结构；不要求智能体共享全局计划或遵循预设轨迹。我们方法的核心是构建一个由实际智能体位置的旋转所诱导的单射虚拟嵌入。该嵌入作为一种保结构映射，所有智能体围绕其稳定相对位置，并允许使用成熟的线性控制技术。我们构建的嵌入在拓扑上等价于期望轨迹（即同胚），从而保持稳定性特征。我们通过在 Quanser QDrone 四旋翼无人机集群上实施该方案，展示了此方法的通用性。结果表明，四旋翼无人机能够自组织形成期望轨迹，同时保持均匀的间距。
英文摘要：
This work investigates the self-organization of multi-agent systems into closed trajectories, a common requirement in unmanned aerial vehicle (UAV) surveillance tasks. In such scenarios, smooth, unbiased control signals save energy and mitigate mechanical strain. We propose a decentralized control system architecture that produces a globally stable emergent structure from local observations only; there is no requirement for agents to share a global plan or follow prescribed trajectories. Central to our approach is the formulation of an injective virtual embedding induced by rotations from the actual agent positions. This embedding serves as a structure-preserving map around which all agent stabilize their relative positions and permits the use of well-established linear control techniques. We construct the embedding such that it is topologically equivalent to the desired trajectory (i.e., a homeomorphism), thereby preserving the stability characteristics. We demonstrate the versatility of this approach through implementation on a swarm of Quanser QDrone quadcopters. Results demonstrate the quadcopters self-organize into the desired trajectory while maintaining even separation.
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。
