基于深度学习的接触力控制用于机器人腿
Hyoseok Lee1, Dongmin Baek2, Hyeokjun Kwon1
1Department of Robot and Smart System Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究介绍了一个深度神经网络 (DNN) 控制器,用于稳定的人形机器人行走. 这种基于DNN的新方法显著改善了接触力控制,与传统方法相比,减少了错误和结算时间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 稳定的接触力控制对于人形机器人在双脚移动过程中的平衡至关重要.
- 通常用于此目的的入口控制器,与机器人系统固有的非线性作斗争.
研究的目的:
- 开发基于学习的接触力控制器,克服传统入口控制器的局限性.
- 为了提高人形机器人力量控制的稳定性和性能.
主要方法:
- 深度神经网络 (DNN) 被用作反向模型,使用输入输出数据捕获系统非线性.
- 整合了一个比例积分 (PI) 控制器,以最大限度地减少稳定状态错误.
- 控制器根据力和高度测量计算目标脚高度,而不需要动态机器人模型.
主要成果:
- 拟议的基于DNN的控制器在接入控制器上显示了显著的性能改进.
- 在步骤响应中,超标被平均减少了96%和结算时间减少了61%.
- 力量跟踪的平方根平均误差 (RMSE) 在阶段和正弦实验中平均下降了66.3%.
结论:
- 基于学习的接触力控制器有效地提高了稳定性,并减少了人形机器人运动中的错误.
- 这种方法为复杂的机器人系统中控制接触力的传统方法提供了强大的替代方案.
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