Fault-Tolerant Pattern Formation by Multiple Robots: A Learning Approach

Jia Wang, Jiannong Cao, Shan Jiang
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引用次数: 3

Abstract

In the field of multi-robot system, the problem of pattern formation has attracted considerable attention. However, the faulty sensor input of each robot is crucial for such system to act reliably in practice. Existing works focus on assuming certain noise model and reducing the noise impact. In this work, we propose to use a learning-based method to overcome this kind of barrier. By interacting with the environment, each robot learns to adapt its behavior to eliminate the malfunctions in the sensors and the actuators. Moreover, we plan to evaluate the proposed algorithms by deploying it into the multi-robot platform developed in our research lab
多机器人的容错模式形成:一种学习方法
在多机器人系统领域中,模式形成问题引起了广泛的关注。然而,每个机器人的故障传感器输入对该系统在实践中可靠运行至关重要。现有的工作重点是假设一定的噪声模型,减少噪声的影响。在这项工作中,我们建议使用基于学习的方法来克服这种障碍。通过与环境的互动,每个机器人学会调整自己的行为,以消除传感器和执行器的故障。此外,我们计划通过将其部署到我们研究实验室开发的多机器人平台来评估所提出的算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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