基于路径的深度学习模型的评估,用于解决反向动力学在旋转 prismatic 机器人中的反向动力学
Navya Manjegowda1, Muralidhara Rao2
1Department of Mathematics, Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Nitte, India.
Scientific reports
|September 30, 2025
概括
深度学习模型被测试为机器人反向动力学. 一个具有k-fold交叉验证的深度前神经网络在预测2-DOF操纵器的关节配置方面取得了最佳的准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 控制系统 控制系统
背景情况:
- 逆动力学 (IK) 问题对于机器人控制至关重要,它可以从端效应器位置确定关节配置.
- 深度学习 (DL) 模型为复杂的IK问题提供了潜在的解决方案,但它们的概括性能需要彻底评估.
- 在不同工作区域中评估DL模型对于强大的机器人应用程序至关重要.
研究的目的:
- 评估各种深度学习模型对逆动力学 (IK) 问题的概括性能.
- 为了比较不同的神经网络架构和训练策略,为一个2度自由度 (DOF) 革命 - Prismatic (RP) 机器人操纵器.
- 确定最有效的DL方法,在不同工作空间区域准确预测联合配置.
主要方法:
- 研究了具有和没有k-fold交叉验证 (CV) 的深度前神经网络 (DFNN),以及像LSTM和GRU这样的循环网络.
- 采用了两种培训策略:基于象限的培训和全职培训.
- 在每个象限和整个工作空间内的预定义的方形和圆形路径上评估模型,测量笛卡尔偏差误差.
主要成果:
- 具有k-fold CV (2输入,1输出) 的DFNN表现出卓越的性能,在所有测试的路径和象限中实现了最低的笛卡尔偏差误差.
- 例如,正方形路径上的误差为0.289毫米 (Q1),0.410毫米 (Q2),0.508毫米 (Q3) 和0.715毫米 (Q4).
- 其他模型,包括DFNN (2输入,2输出),没有CV的DFNN,LSTM和GRU,显示出明显更高的错误.
结论:
- 采用单个输出配方的k倍CV增强DFNN为IK问题提供了高度准确和可通用的解决方案.
- 这种方法有效地处理机器人操纵器联合解决方案中固有的奇点和模糊性.
- 这些发现强调了强大的验证技术的重要性,如k-fold CV用于机器人深度学习.
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