论文标题
动态和分布式优化用于空中群体车辆的分配
Dynamic and Distributed Optimization for the Allocation of Aerial Swarm Vehicles
论文作者
论文摘要
最佳运输（OT）是一个框架，可以指导多个来源和目标网络中有效的资源分配策略的设计。本文以新颖的方式将离散ot应用于无人机，以实现适当的任务分配和执行。无人机群部署已经在使用传感器来获取环境知识的多个域中运行[1]。用例，例如化学和辐射检测，热和RGB成像产生了对算法的特定需求，该算法考虑了无人机和航点端上的参数，并允许随着环境中的群集获得信息而更新匹配方案。此外，可以使用分布式算法可以根据Swarm网络或参数的更改动态更新的分布式算法来删除对集中规划器的需求。为此，我们开发了一种动态和分布式的OT算法，该算法将基于无人机的一个参数和Waypoint上的另一个参数匹配无人机与最佳航路点。我们通过案例研究显示了该算法的收敛性和分配，并测试了算法对模拟中贪婪分配算法的有效性。
Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. This paper applies discrete OT to a swarm of UAVs in a novel way to achieve appropriate task allocation and execution. Drone swarm deployments already operate in multiple domains where sensors are used to gain knowledge of an environment [1]. Use cases such as, chemical and radiation detection, and thermal and RGB imaging create a specific need for an algorithm that considers parameters on both the UAV and waypoint side and allows for updating the matching scheme as the swarm gains information from the environment. Additionally, the need for a centralized planner can be removed by using a distributed algorithm that can dynamically update based on changes in the swarm network or parameters. To this end, we develop a dynamic and distributed OT algorithm that matches a UAV to the optimal waypoint based on one parameter at the UAV and another parameter at the waypoint. We show the convergence and allocation of the algorithm through a case study and test the algorithm's effectiveness against a greedy assignment algorithm in simulation.
