论文标题
多代理空间预测控制，并应用于无人机羊群（扩展版）
Multi-Agent Spatial Predictive Control with Application to Drone Flocking (Extended Version)
论文作者
论文摘要
我们介绍了空间预测控制（SPC）的新颖概念来解决以下问题：给定具有位置低级控制器（LLC）的代理（例如无人机）和特定于任务的分布式成本功能，分布式控制器如何实现和维护成本函数如何最小化，而没有工厂模型和仅在环境中的位置观察，我们的完全分布的SPC控制器严格基于代理本身以及其相邻代理的位置。这些信息在每个时间步骤中都用于计算成本函数的梯度并执行空间外观，以预测LLC的最佳下一个目标位置。使用高保真模拟环境，我们表明SPC在无人机羊群上优于最密切相关的控制器，潜在的现场控制器类别。我们还表明，SPC能够通过展示其在真实硬件上的性能来应对潜在的SIM到实现传输差距，即我们使用9个Crazyflie 2.1无人机实施羊群。
We introduce the novel concept of Spatial Predictive Control (SPC) to solve the following problem: given a collection of agents (e.g., drones) with positional low-level controllers (LLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time-step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the LLC. Using a high-fidelity simulation environment, we show that SPC outperforms the most closely related class of controllers, Potential Field Controllers, on the drone flocking problem. We also show that SPC is able to cope with a potential sim-to-real transfer gap by demonstrating its performance on real hardware, namely our implementation of flocking using nine Crazyflie 2.1 drones.
