A synchronous control strategy of robot social behavior driven by scenario information and neural modulation mechanism

IF 6.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2025-03-06 DOI:10.1016/j.isatra.2025.03.002

Renkai Liu , Xiaorui Liu , Hang Su , Jinpeng Yu

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引用次数: 0

Abstract

Robots have been widely employed in scenarios that involve various environmental factors and social individuals. As one kind of social companion, robot is supposed to obey human social protocol and display anthropomorphic behaviors. In this paper, we focus on the problem of robot behavior control in multi-individuals scenarios, and build a coordinated robot behavior model containing body movement/orientation, head rotation and eyeball movement. Within the proposed model, a synchronous control strategy based on social space theory and neural modulation mechanism is proposed. This strategy collects RGB-D camera stream and acoustic field data perceived from multi-individuals scenario, and controls the robot to complete movement and social gaze behaviors. As for the eye-head coordinated gaze behavior, it is modulated by a novel optimal control algorithm based on the minimum neural transmission noise. Above works are validated on the Xiaopang robot platform, the experimental observations indicate that the robot can achieve anthropomorphic response in dynamic multi-individuals scenario. Within above promising results, the effectiveness of this strategies could be proven

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由场景信息和神经调制机制驱动的机器人社交行为同步控制策略。

机器人已被广泛应用于涉及各种环境因素和社会个体的场景中。机器人作为一种社会伴侣，应该遵守人类的社会礼仪，表现出拟人化的行为。本文针对多个体场景下的机器人行为控制问题，构建了包含身体运动/定向、头部旋转和眼球运动的机器人协调行为模型。在该模型中，提出了一种基于社会空间理论和神经调节机制的同步控制策略。该策略收集多个体场景感知到的RGB-D摄像头流和声场数据，控制机器人完成运动和社交注视行为。对于眼-头协同注视行为，采用一种基于最小神经传递噪声的最优控制算法进行调节。上述工作在小鹏机器人平台上进行了验证，实验观察表明，该机器人能够在动态多个体场景下实现拟人响应。在上述有希望的结果中，可以证明这种策略的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.