Active Target Tracking With Self-Triggered Communications in Multi-Robot Teams
Active Target Tracking With Self-Triggered Communications in Multi-Robot Teams
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DOI：
10.1109/tase.2018.2867189
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发表时间：
2017-04
影响因子：
5.6
通讯作者：
Lifeng Zhou;Pratap Tokekar
中科院分区：
文献类型：
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作者：
Lifeng Zhou;Pratap Tokekar
研究了分布式目标跟踪中减少通信量的问题。我们专注于场景，其中一组机器人被允许在环境的边界上移动。他们的目标是寻找一个编队，以便最好地跟踪在环境内部移动的目标。机器人能够测量到目标的距离。分散控制策略已被提出，在过去，这保证了机器人渐近收敛到最优编队。然而，现有的方法要求机器人与他们的邻居在所有的时间步交换信息。相反，我们专注于分散策略，以减少机器人之间的通信量。我们提出了一个自触发的通信策略，决定一个特定的机器人应该寻求最新的信息，从它的邻居，当它是安全的操作可能过时的信息。我们证明了当目标静止时，该策略渐近收敛到期望编队。对于一个移动的目标的情况下，我们使用一个分散的卡尔曼滤波器与协方差交叉共享相邻机器人的信念。我们通过模拟和概念验证实验来评估所有的方法。从业者注意-我们研究的问题跟踪一个目标使用一组协调机器人。目标跟踪问题在许多应用中普遍存在，例如协作机器人、监控和野生动物监测。机器人之间的协调通常需要它们之间的通信。大多数多机器人协调算法隐含地假设机器人可以在所有的时间步长进行通信。通信可能是一个相当大的能源消耗来源，特别是对于小型机器人。此外，在许多设置中，在所有时间步长处进行通信可能是多余的。以此为动机，我们提出了一种算法，其中机器人不一定在任何时候都进行通信，而是选择特定的触发时间实例与邻居共享信息。尽管有限的通信的限制，我们表明，该算法收敛到最佳配置无论是在理论上，以及在模拟。
We study the problem of reducing the amount of communication in decentralized target tracking. We focus on the scenario, where a team of robots is allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past, which guarantees that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on decentralized strategies to reduce the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information. We prove that this strategy converges asymptotically to the desired formation when the target is stationary. For the case of a mobile target, we use a decentralized Kalman filter with covariance intersection to share the beliefs of neighboring robots. We evaluate all the approaches through simulations and a proof-of-concept experiment. Note to Practitioners—We study the problem of tracking a target using a team of coordinating robots. Target tracking problems are prevalent in a number of applications, such as co-robots, surveillance, and wildlife monitoring. Coordination between robots typically requires communication amongst them. Most multi-robot coordination algorithms implicitly assume that the robots can communicate at all time steps. Communication can be a considerable source of energy consumption, especially for small robots. Furthermore, communicating at all time steps may be redundant in many settings. With this as motivation, we propose an algorithm where the robots do not necessarily communicate at all times and instead choose specific triggering time instances to share information with their neighbors. Despite the limitation of limited communication, we show that the algorithm converges to the optimal configuration both in theory as well as in simulations.