基于多模态传感器和机器人行为的鲁棒机器人对人的关注

Sangseok Yun, C. G. Kim, Munsang Kim, Mun-Taek Choi
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引用次数: 4

摘要

本文提出了基于多模态传感器和机器人行为的鲁棒机器人对人的关注。所有需要关注的机器人部件都是在智能机器人软件架构上运行的。人体搜索是在各种光照变化和动态环境下,从视觉和语音感知信息中收集人体信息。人体跟踪器可以高效、安全地跟踪人脸轨迹。与普遍的看法不同,机器人最大的障碍和竞争因素预计是人机交互。由于机器人智能尚未达到实用水平,因此使用智能系统创建通用交互管理器在一段时间内将无法实现。相反,它侧重于基于情感的单向说话和对人类的表达。实验结果表明,该方法在实际环境中是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust robot's attention for human based on the multi-modal sensor and robot behavior
In this paper, we propose the robust robot's attention for human based on the multi-modal sensor and robot behavior. All of the robot components for attention are operating on the intelligent robot software architecture. Human search collect the human information from the sensing information for vision and voice in various illumination change and dynamic environments. And human tracker follows the face trajectory with efficiency and safety. Unlike common belief, the biggest obstacle and competitive factor in robotics is expected to be human robot interaction. Since robot intelligence is not yet at a practical level, the creation of a general interaction manager using an intelligence system will not be realized for some time. Instead, it focuses on one-way speaking and expressing based on emotion to human. Experimental results show that the proposed scheme works successfully in real environments.
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