Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism

Yuming XIANG; Kun CHEN; Zhifeng ZHAO; Rongpeng Li; Honggang ZHANG

doi:10.11959/j.issn.2096-6652.202215

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Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism

Special Topic: Crowd Intelligence | 更新时间：2024-06-05

- Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 1, Pages: 84-96(2022)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202215
  CLC： TP18
- Published Online：2022-03，
  
  Published：15 March 2022
- 稿件说明：
移动端阅览
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：

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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