首页> 外文会议>Machine intelligence and bio-inspired computation: theory and applications VIII >A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems
【24h】

A reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems

机译:强化学习训练的模糊神经网络控制器,用于维持多机器人系统中的无线通信连接

获取原文
获取原文并翻译 | 示例

摘要

This paper presents a decentralized multi-robot motion control strategy to facilitate a multi-robot system, comprised of collaborative mobile robots coordinated through wireless communications, to form and maintain desired wireless communication coverage in a realistic environment with unstable wireless signaling condition. A fuzzy neural network controller is proposed for each robot to maintain the wireless link quality with its neighbors. The controller is trained through reinforcement learning to establish the relationship between the wireless link quality and robot motion decision, via consecutive interactions between the controller and environment. The tuned fuzzy neural network controller is applied to a multi-robot deployment process to form and maintain desired wireless communication coverage. The effectiveness of the proposed control scheme is verified through simulations under different wireless signal propagation conditions.
机译:本文提出了一种分散的多机器人运动控制策略,以促进多机器人系统的发展,该系统包括通过无线通信进行协调的协作移动机器人,以在不稳定的无线信令条件下的现实环境中形成并维持所需的无线通信覆盖范围。针对每个机器人,提出了一种模糊神经网络控制器,以维持与其邻居的无线链接质量。通过强化学习对控制器进行培训,以通过控制器与环境之间的连续交互来建立无线链路质量与机器人运动决策之间的关系。调整后的模糊神经网络控制器被应用于多机器人部署过程,以形成并维持所需的无线通信覆盖范围。通过在不同无线信号传播条件下的仿真,验证了所提出控制方案的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号