分布式多机器人非合作放牧控制
Distributed Multirobot Control for Non-Cooperative Herding
摘要：
在本文中，我们考虑的问题，保护一个高价值的区域被突破羊代理人的手工制作运动狗机器人。我们使用控制障碍函数对狗的速度施加限制，从而引起羊相对于高值区域的排斥。本文从三个方面扩展了我们以前在同一主题上的工作中所得到的结果。首先，我们在许多机器人上实现并验证了我们以前开发的集中式放牧算法。我们展示了使用三个狗机器人放牧多达五只羊的代理。其次，作为集中式方法的扩展，我们开发了两个分布式羊群算法，一个有利于可行性，而其他有利于最优性。在第一个算法中，我们将一只独特的羊分配给一只独特的狗，让那只狗负责将分配给它的羊从保护区赶出去。我们提供了这种方法的可行性证明，以及数值模拟。在第二个算法中，我们开发了一个迭代的分布式重新制定的集中式算法，它继承了集中式方法的最优性（即预算效率）。最后，我们进行了真实世界的实验，这些分布式算法，并演示了放牧多达五个羊代理使用五个狗机器人。
英文摘要：
In this paper, we consider the problem of protecting a high-value area from being breached by sheep agents by crafting motions for dog robots. We use control barrier functions to pose constraints on the dogs' velocities that induce repulsion in the sheep relative to the high-value area. This paper extends the results developed in our prior work on the same topic in three ways. Firstly, we implement and validate our previously developed centralized herding algorithm on many robots. We show herding of up to five sheep agents using three dog robots. Secondly, as an extension to the centralized approach, we develop two distributed herding algorithms, one favoring feasibility while the other favoring optimality. In the first algorithm, we allocate a unique sheep to a unique dog, making that dog responsible for herding its allocated sheep away from the protected zone. We provide feasibility proof for this approach, along with numerical simulations. In the second algorithm, we develop an iterative distributed reformulation of the centralized algorithm, which inherits the optimality (i.e. budget efficiency) from the centralized approach. Lastly, we conduct real-world experiments of these distributed algorithms and demonstrate herding of up to five sheep agents using five dog robots.
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