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软机器人操纵器在非结构化环境中的目标识别和抓取策略

Lisong Dong¹, Huiru Zhu¹, Yuan Chen¹

¹Hefei University of Technology, School of Mechanical Engineering, Hefei 230009, China.

The Review of scientific instruments

|September 5, 2025

概括

这项研究介绍了一只柔软的机器人手和一个增强的You Only Look Once v5s (YOLOv5s) 算法,用于更好地抓住不规则的物体. 该系统在精度和功率掌握任务中取得了82%的成功率.

科学领域:

机器人技术
计算机视觉
材料科学

背景情况:

机器人很难在没有结构的环境中抓住不规则和脆弱的物体.
现有的方法对于微妙的操作任务缺乏效率和准确性.

研究的目的:

开发一个柔软的机器人手和一个增强的物体检测算法,
为了提高实时机器人应用的You Only Look Once v5s (YOLOv5s) 算法的性能.

主要方法:

设计了一种基于气动网络的快速软指结构,并使用了经过验证的指曲的数学模型.
通过整合协调注意 (CA) 和完善空间金字塔聚合 (SPP) 模块来增强YOLOv5s.
开发了一种软机器人抓取实验平台,用于精确和强度抓取测试.

主要成果:

增强的YOLOv5s-CA-SPP模型显示了平均精度和识别速度的改善.
实验分析证实了软机器人手和改进的检测算法的有效性.
该系统在最佳姿势的实验中取得了82%的成功率.

结论:

集成软机器人手和增强的YOLOv5s算法为复杂的抓取任务提供了可行的解决方案.
开发的系统在非结构化机器人操纵中具有很大的应用潜力.
进一步的研究可以探索先进的控制策略和更广泛的对象识别能力.

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