Active target tracking with self-triggered communications
Active target tracking with self-triggered communications
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DOI：
10.1109/icra.2017.7989244
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发表时间：
2017-05
期刊：
影响因子：
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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问题中减少通信量的问题。我们专注于一组机器人被允许在环境的边界上移动的场景。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，保证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从邻居。我们证明了这种策略收敛到一个最优的形成。我们比较了这两种方法（恒定通信和自触发通信）通过跟踪静止和移动的目标的仿真。
We study the problem of reducing the amount of communication in a distributed target tracking problem. We focus on the scenario where a team of robots are 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 that guarantee 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 reducing 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a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information from the neighbor. We prove that this strategy converges to an optimal formation. We compare the two approaches (constant communication and self-triggered communication) through simulations of tracking stationary and mobile targets.