期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 2, 页码 5552-5559出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3146524
关键词
Deep learning methods; multi-robot systems; networked robots
类别
资金
- National Science Foundation Graduate Research Fellowship [DGE-1845298]
- ARL [DCIST CRA W911NF-17-2-0181]
- NSF [CNS-1521617]
- ARO [W911NF-13-1-0350]
- ONR [N00014-20-1-2822, N00014-20-S-B001]
- Qualcomm Research
- NVIDIA Corporation
The research team proposed a data-driven approach using convolutional neural networks to optimize the algebraic connectivity of robot teams. This method has the potential for online applications and is significantly faster than traditional optimization-based approaches.
In this letter we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots. While a considerable amount of research has been devoted to this problem, we lack a method that scales in a manner suitable for online applications for more than a handful of agents. To that end, we propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert that uses an optimization-based strategy. We demonstrate the performance of our CNN on canonical line and ring topologies, 105 k randomly generated test cases, and larger teams not seen during training. We also show how our system can be applied to dynamic robot teams through a Unity-based simulation. After training, our system produces connected configurations over an order of magnitude faster than the optimization-based scheme for teams of 10-20 agents.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据