异步和动态覆盖控制方案用于持续监视任务
Asynchronous and Dynamic Coverage Control Scheme for Persistent Surveillance Missions
AI总结：
本文提出了一种基于分解的多智能体持续监视任务覆盖控制方案，通过模块化框架解耦高层任务分配与低层运动规划，利用中央基站管理覆盖分配和监视参数，并通过非计划和异步交换传输至移动代理，实现负载平衡和有效路径规划。
摘要：
本文提出了一种基于分解的覆盖控制方案，用于在通信受限和动态环境中运行的多智能体持续监视任务。所提出的方法在模块化框架中解耦高层任务分配与低层运动规划。覆盖分配和监视参数由中央基站管理，并通过非计划和异步交换传输至移动代理。覆盖更新促进负载平衡，同时保持几何和时间特征，允许与通用路径规划器有效配对。具体而言，所提出的方案保证了（i）覆盖区域连通且集体覆盖环境，（ii）子区域可能仅在有界时间内未被覆盖，（iii）碰撞（或传感重叠）被内在避免，（iv）在静态事件可能性下，集体覆盖区域收敛到帕累托最优配置。该管理方案随后与满足宽松假设的通用路径规划器配对。通过模拟监视任务进行了说明。
英文摘要：
A decomposition-based coverage control scheme is proposed for multi-agent, persistent surveillance missions operating in a communication-constrained, dynamic environment. The proposed approach decouples high-level task assignment from low-level motion planning in a modular framework. Coverage assignments and surveillance parameters are managed by a central base station, and transmitted to mobile agents via unplanned and asynchronous exchanges. Coverage updates promote load balancing, while maintaining geometric and temporal characteristics that allow effective pairing with generic path planners. Namely, the proposed scheme guarantees that (i) coverage regions are connected and collectively cover the environment, (ii) subregions may only go uncovered for bounded periods of time, (iii) collisions (or sensing overlaps) are inherently avoided, and (iv) under static event likelihoods, the collective coverage regions converge to a Pareto-optimal configuration. This management scheme is then paired with a generic path planner satisfying loose assumptions. The scheme is illustrated through simulated surveillance missions.
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。
