脑注意力机制启发的群体智能协同避障方法

项羽铭; 陈焜; 赵志峰; 李荣鹏; 张宏纲

doi:10.11959/j.issn.2096-6652.202215

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脑注意力机制启发的群体智能协同避障方法

专题：群体智能 | 更新时间：2024-06-05

- 脑注意力机制启发的群体智能协同避障方法
- Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism
- 智能科学与技术学报 2022年4卷第1期页码：84-96
- 作者机构：
  
  1. 浙江大学信息与电子工程学院，浙江杭州 310027
  2. 之江实验室，浙江杭州 311121
- 作者简介：
  
  [ "项羽铭（1999− ），男，浙江大学信息与电子工程学院在读，主要研究方向为群体智能、强化学习" ]
  [ "陈焜（1997− ），男，浙江大学信息与电子工程学院硕士生，主要研究方向为群体智能、强化学习" ]
  [ "赵志峰（1975− ），男，之江实验室总工程师，主要研究方向为认知无线电、无线网格网络和软件定义网络在无线通信中的应用" ]
  [ "李荣鹏（1989− ），男，博士，浙江大学信息与电子工程学院副教授，主要研究方向为智能通信网络、网络智能、网络切片" ]
  [ "张宏纲（1967− ），男，博士，浙江大学电子与信息工程学院教授，主要研究方向为认知无线电、绿色通信和下一代异构蜂窝网络架构" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61731002);国家自然科学基金资助项目;The National Natural Science Foundation of China(62071425);浙江省重点研发计划资助项目;The Key Research and Development Program of Zhejiang Province(2019C01002);浙江省重点研发计划资助项目;The Key Research and Development Program of Zhejiang Province(2019C03131);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province(LY20F010016);华为合作项目;Huawei Cooperation Project
- DOI：10.11959/j.issn.2096-6652.202215
  中图分类号： TP18
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-15
- 稿件说明：
移动端阅览
项羽铭, 陈焜, 赵志峰, 等. 脑注意力机制启发的群体智能协同避障方法[J]. 智能科学与技术学报, 2022,4(1):84-96.

Yuming XIANG, Kun CHEN, Zhifeng ZHAO, et al. Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 84-96.
项羽铭, 陈焜, 赵志峰, 等. 脑注意力机制启发的群体智能协同避障方法[J]. 智能科学与技术学报, 2022,4(1):84-96. DOI： 10.11959/j.issn.2096-6652.202215.

Yuming XIANG, Kun CHEN, Zhifeng ZHAO, et al. Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 84-96. DOI： 10.11959/j.issn.2096-6652.202215.

摘要

现有群体智能系统执行探索任务时往往获取、计算和传输大量冗余信息，造成群智系统资源利用效率低下。设计任务驱动的计算和通信资源高效融合利用机理成为亟待解决的关键科学问题。基于此，提出了一种脑注意力机制启发的群体智能协同避障方法。受脑注意力机制启发，群智系统引入基于深度Q网络的传感器智能选择模块，实现在探索未知环境时高效选择传感器的工作状态，利用尽量少的传感器开销获取和计算关键信息；以最佳交互碰撞避免算法为基础，单一智能体融合邻居智能体的少量关键信息，驱动传感器智能选择模块，在完成集群协同避障的同时，大幅降低传感器获取和计算信息的冗余度。在仿真平台及Kehepera IV机器人实际场景中进行验证，结果显示，所提方法可以显著降低集群系统传感器的信息冗余度，并且随着智能体数量以及信息交互量的增加，性能增益更加显著。

Abstract

A crowd intelligent (CI) system often acquires

calculates

and transmits a large amount of redundant information during the performing of exploration tasks

which inevitably results in inefficient use of the limited resources.Therefore

it emerges a strong incentive to design a task-driven mechanism for efficient utilization of computing and communication resources.A crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism was proposed.Inspired by brain attention mechanism

the CI system introduced an intelligent selection module based on the deep Q network

by efficiently tuning the working state of sensors exploring the unknown environment and realized the acquisition and calculation of key necessary information with as little sensor overhead as possible.Meanwhile

based on the optimal reciprocal collision avoidance algorithm

a single agent fuses a small amount of limited information from neighbor agents to drive the intelligent selection module

so as to greatly reduce the redundancy of sensor acquisition and information calculation required for the obstacle avoidance task.The effectiveness of this proposed method was verified through extensive simulation analyses and practical realization empowered with Kehepera IV robots.The results show that the proposed method can significantly reduce the redundancy of sensor information in the CI system.More importantly

as the number of agents and the amount of information interaction increase

there also emerged a clear trend in the increase of performance gains.

关键词

Keywords

references

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