优化逆动力学建模和关节角度预测,用于具有可解释AI的六度自由度人形机器人
Rakesh Chandra Joshi1, Jaynendra Kumar Rai2, Radim Burget3
1Amity Centre for Artificial Intelligence, Amity University, Noida, UP, India.
ISA transactions
|December 20, 2024
概括
本研究探讨了人工智能模型,以解决六度自由度 (六度DoF) 人类型机器人的复杂反向动力学. 最优的人工智能模型平衡了准确性和计算效率,以实现实际的机器人自动化.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算动力学计算动力学
背景情况:
- 反向动力学对于机器人控制至关重要,它可以确定所需端效应器姿势的关节配置.
- 六度自由度 (six-DoF) 的人类型机器人由于复杂的数学,非线性和多个解决方案而带来了重大的反向动力学挑战.
- 现有的方法经常与计算需求和通用性作斗争.
研究的目的:
- 系统地探索和确定最佳的人工智能 (AI) 模型,以解决六个DoF的人形机器人的反动力学问题.
- 在人工智能驱动的反动力学解决方案中平衡预测准确性和计算效率.
- 使用可解释AI (XAI) 技术增强模型的解释性.
主要方法:
- 系统地探索和严格评估各种人工智能模型.
- 贝叶斯优化用于超参数调整,以选择最佳回归器.
- 在性能评估的公共数据集上进行五重交叉验证.
- 可解释性AI (XAI) 使用SHAP (夏普利添加式扩展) 进行特征重要性分析.
主要成果:
- 选择的AI模型在预测六个连接角度方面取得了很高的准确性,平均平方误差在1.934 × 10-3和3.522 × 10-3之间.
- 证明了显著的计算效率,每个样本的预测时间约为1.25毫秒.
- SHAP分析证实了模型的可解释性,并突出了关键特征的重要性.
结论:
- 人工智能模型为六个DoF的人类型机器人的复杂反向动力学提供了可行和高效的解决方案.
- 开发的方法成功地平衡了预测准确性和计算速度,这对于现实世界机器人自动化至关重要.
- 这项研究通过为机器人系统提供可解释和高效的动力学解决方案,推进了最先进的技术.
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