通过早期融合深度学习增强视觉反控制
Adrian-Paul Botezatu1, Lavinia-Eugenia Ferariu1, Adrian Burlacu1
1Faculty of Automatic Control and Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, D. Mangeron 27, 700050 Iasi, Romania.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
具有早期融合的深度学习模型可以增强视觉服务系统. 整合图像时刻和细分地图可以提高6DOF机器人的机器人控制精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视觉伺服系统使用视觉反来控制机器人.
- 深度学习模型处理视觉数据用于机器人运动计算.
- 早期融合将额外的信息整合到神经网络输入中.
研究的目的:
- 通过深度模型研究用于视觉伺服的早期融合技术.
- 分析图像时刻,细分和特征点对控制精度的影响.
- 为了确定机器人运动控制的辅助地图的最佳细节水平.
主要方法:
- 通过将图像时刻,基于区域的细分和特征点集成到深度模型中来实现早期融合.
- 应用这些技术单独和组合生成不同细节的地图.
- 在6度自由度的机器人控制任务上实验评估了这些方法的性能.
主要成果:
- 早期融合显著改善了线性和角度相机速度的近似值.
- 提供中低级细节的辅助地图产生了最好的控制结果.
- 技术的组合在控制机器人在不同配置之间的移动方面表现出有效性.
结论:
- 早期融合是增强视觉伺服系统的有益策略.
- 图像时刻和基于细分的地图为机器人控制提供了宝贵的信息.
- 优化输入数据的详细程度对于实现精确的机器人运动控制至关重要.
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