脑注意力机制启发的群体智能协同避障方法

doi:10.11959/j.issn.2096-6652.202215

Abstract

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.

Key words: crowd intelligent, optimization of resource utilization efficiency, deep Q network, optimal reciprocal collision avoidance

CLC Number:

TP18

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.

Figures/Tables 11

References 26

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方法	3个机器人	4个机器人	8个机器人
持续采样法（基线）	315.0	383.75	586.86
混合高斯模型选择	238.67 / 24.2%	303.75 / 20.8%	419.75 / 28.5%
DQN智能选择	198.0 / 37.1%	257.25 / 33.0%	—
混合高斯模型选择 + {障碍物，传感器}信息（实验4）	174.0 / 44.8%	172.0 / 55.2%	241.0 / 58.9%
DQN智能选择 + {障碍物，传感器}信息（实验5）	147.0 / 53.3%	155.5 / 59.5%	220.86 / 62.4%

Crowd intelligent cooperative obstacle avoidance method inspired by brain attention mechanism

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References 26

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