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基于预测的步骤可行性的双步行稳定性分类.

Pedro Parik-Americano1, Jorge Igual2, Larissa Driemeier1

  • 1Biomechatronics Laboratory Mechatronics Department, Escola Politécnica, University of São Paulo (EP-USP), São Paulo 05508-030, Brazil.

Biomimetics (Basel, Switzerland)
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概括
此摘要是机器生成的。

这项研究通过使用机器学习分类器来预测步骤可行性,简化了稳定的双脚机器人步行. 这种方法可以实现机器人和外骨的快速,低成本的步态规划.

关键词:
双脚的稳定性 双脚的稳定性这是分类分类的分类.预测的步骤可行性可行性.实时应用程序实时应用程序

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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:

  • 双脚行走机器人和外骨需要强大的稳定性控制.
  • 预测步骤可行性 (PSV) 算法提供了先进的步态规划,但在计算上是密集的.
  • 实时实施PSV等复杂的优化标准是一个重大挑战.

研究的目的:

  • 评估实时实施PSV算法的可行性. 在双脚运动中.
  • 通过使用机器学习分类器来预测步骤稳定性来降低PSV的计算成本.
  • 评估各种分类算法的性能和用于预测步行稳定的堆叠策略.

主要方法:

  • 使用PSV模拟生成了三组日益复杂的数据集.
  • 训练并评估了11个分类算法 (包括kNN,SVM,决策树,随机森林,MLP) 来预测步骤稳定性.
  • 使用堆叠策略与分类器结合使用,以提高预测准确度.
  • 测量每个方法的计算成本和预测时间.

主要成果:

  • k 最接近的邻居,支持向量机 (RBF内核),决策树和随机森林在预测步骤可行性方面表现优越.
  • 多层感知器也表现出一致的,强的性能.
  • 基于线性的算法表现出较低的预测能力.
  • 堆叠策略没有产生显著的性能改善.
  • 分类器实现了快速计算 (<1毫秒),计算开销最小.

结论:

  • 机器学习分类器可以有效地预测双脚机器人的步骤可行性,大大降低了计算成本.
  • 开发的特征向量和分类方法可以适应不同的机器人模型和数据集.
  • 充分和平衡的训练数据对于可靠的分类器性能在步态稳定性预测中至关重要.
  • 这种方法可以实现有效的实时步态规划,这对于稳定的双脚运动至关重要.