A Best-Response Algorithm With Voluntary Communication and Mobility Protocols for Mobile Autonomous Teams Solving the Target Assignment Problem
A Best-Response Algorithm With Voluntary Communication and Mobility Protocols for Mobile Autonomous Teams Solving the Target Assignment Problem
复制标题
DOI：
10.1109/tcst.2023.3296003
复制
发表时间：
2020-03
影响因子：
4.8
通讯作者：
Sarper Aydın;Ceyhun Eksin
中科院分区：
文献类型：
--
作者：
Sarper Aydın;Ceyhun Eksin
我们考虑一个由移动自动驾驶机器人组成的团队，目的是涵盖一组目标。每个机器人旨在选择一个目标，并在最后一次与其他机器人进行协调的最后时间覆盖并实现了目标。机器人没有意识到其他机器人打算覆盖的目标。每个机器人都可以控制其移动性并将信息发送到谁。我们假设通信发生在无线通道上发生的，该通道可能会逐渐消失和失败。鉴于设置，我们根据分散的虚拟游戏（DFP）提出了一种分散的算法，在该算法中，机器人推理了其他机器人的选择和位置，以决定选择哪种目标，是否要进行交流，与谁进行交流以及在哪里移动。具体而言，机器人的沟通动作是学习意识的，其移动性动作对沟通的成功概率很敏感。我们表明，分散算法保证机器人将在有限的时间内覆盖目标。使用一组移动机器人的数值模拟和实验在有限的时间内确认目标覆盖范围，并表明与基准分布式算法相比，用于通信和学习意识的交流协议的移动性控制减少了通信尝试的数量，该算法依赖于每个决策时期的通信。
We consider a team of mobile autonomous robots with the aim to cover a given set of targets. Each robot aims to select a target to cover and physically reach it by the final time in coordination with other robots given the locations of targets. Robots are unaware of which targets other robots intend to cover. Each robot can control its mobility and who to send information to. We assume communication happens over a wireless channel that is subject to fading and failures. Given the setup, we propose a decentralized algorithm based on decentralized fictitious play (DFP) in which robots reason about the selections and locations of other robots to decide which target to select, whether to communicate or not, who to communicate with, and where to move. Specifically, the communication actions of the robots are learning-aware, and their mobility actions are sensitive to the success probability of communication. We show that the decentralized algorithm guarantees that robots will cover the targets in finite time. Numerical simulations and experiments using a team of mobile robots confirm the target coverage in finite time and show that mobility control for communication and learning-aware communication protocols reduce the number of communication attempts in comparison to a benchmark distributed algorithm that relies on communication at each decision epoch.