基于pso - ml - lstm的机械手遥操作IMU状态估计方法。

IF 3 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-09-16 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1638853
Renyi Zhou, Yuanchong Li, Aimin Zhang, Tie Zhang, Yisheng Guan, Zhijia Zhao, Shouyan Chen
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引用次数: 0

摘要

机械手远程操作可以把人从危险的工作中解放出来。环境干扰和设备固有特性所产生的信号噪声会限制远程操作的性能。提出了一种基于粒子群优化(PSO)和调制长短期记忆(ML-LSTM)神经网络的惯性测量单元状态估计方法,以减轻惯性测量单元累积误差对机器人遥操作性能的影响。首先基于全局构型参数和混合映射方法建立了人臂和七自由度机械臂的运动映射模型。该模型用于描述IMU累积误差对机器人遥操作性能的影响。随后,利用粒子群算法和ML-LSTM神经网络构建了IMU姿态状态估计模型。利用多个imu和手柄的初始数据对估计模型进行训练。最后,通过对比实验验证了所提状态估计模型的性能。结果表明,PSO-ML-LSTM算法能有效消除IMU累积误差对机器人遥操作的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A PSO-ML-LSTM-based IMU state estimation approach for manipulator teleoperation.

Manipulator teleoperation can liberate humans from hazardous tasks. Signal noise caused by environmental disturbances and the devices' inherent characteristics may limit the teleoperation performance. This paper proposes an approach for inertial measurement unit (IMU) state estimation based on particle swarm optimization (PSO) and modulated long short-term memory (ML-LSTM) neural networks to mitigate the impact of IMU cumulative error on the robot teleoperation performance. A motion mapping model for the human arm and a seven-degree-of-freedom (7-DOF) robotic arm are first established based on global configuration parameters and a hybrid mapping method. This model is used to describe the impact of IMU cumulative error on the robot teleoperation performance. Subsequently, the IMU pose state estimation model is constructed using PSO and ML-LSTM neural networks. The initial data of multiple IMUs and handling handles are used for training the estimation model. Finally, comparative experiments are conducted to verify the performance of the proposed state estimation model. The results demonstrate that the PSO-ML-LSTM algorithm can effectively eliminate the impact of IMU cumulative errors on robot teleoperation.

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来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
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