机械工程

未知人体作业环境下基于力信息的机器人法向姿态跟踪方法

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  • 华南理工大学 机械与汽车工程学院,广东 广州 510640

网络出版日期: 2025-09-09

Robot Normal Posture Tracking Method Based on Force Information in Unknown Human Work Environments

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  • School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China

Online published: 2025-09-09

摘要

在医疗康复与美容护理等人机交互作业场景中,机器人需在人体肌肤表面维持稳定的接触法向姿态。人体柔性曲面轮廓由于个体和作业区域差异性、作业过程体位变化和实时形变等诸多不确定性限制机器人姿态跟踪性能。针对该问题,在人机力与运动交互作业系统上建立多个辅助坐标系,描述和分析交互过程中运动以及力与力矩关系,并构建坐标系间的变换矩阵。融合弹性力学赫兹接触原理和生物力学黏着摩擦力模型,研究一种面向刚性球形末端执行器与软组织接触运动下的基于六维力信息的法向量关系模型,模型实时求解获取当前法向姿态。为确保获取六维力信息的准确性,采用了二次重力补偿与周期误差力矩补偿双重补偿机制。研究成果通过力传感器信息实现未知人体软组织曲面的机器人法向姿态跟踪方法,在人脸模型上从眉心经鼻梁至鼻尖轨迹的法向姿态控制实验中,协同法向力柔顺控制的阻抗算法,法向姿态控制偏差范围为1.12°-3.2°,验证了控制方法的有效性,提升了机器人在非结构化人体作业环境中的自适应性。

本文引用格式

翟敬梅, 钟嘉栋 . 未知人体作业环境下基于力信息的机器人法向姿态跟踪方法[J]. 华南理工大学学报(自然科学版), 0 : 1 . DOI: 10.12141/j.issn.1000-565X.250232

Abstract

In human-robot interaction scenarios such as medical rehabilitation and cosmetic care, a robot must maintain a stable contact posture normal to the skin surface. The highly compliant, non-uniform contours of human tissue—together with posture changes and real-time deformation during operation—severely limit conventional attitude-tracking performance. To address this challenge, we establish multiple auxiliary coordinate frames on the force-motion interaction system, describe and analyze the kinematics as well as the force/torque relationships during contact, and construct the corresponding transformation matrices. By combining Hertzian elastic contact theory with a biomechanical adhesion–friction model, we develop a normal-vector relationship model for a rigid spherical end-effector interacting with soft tissue, based on six-axis force measurements, and the model solves in real-time to obtain the current normal attitude. To ensure the accuracy of the six-axis force data, a dual compensation scheme—secondary gravity compensation and periodic torque-error compensation—is implemented. The proposed method enables real-time estimation of the unknown surface normal of soft human tissue through force-sensor feedback. Experiments on a facial model, tracking the trajectory from the glabella along the nasal dorsum to the tip, demonstrate that the normal-attitude error remains within 1.12°–3.2°under an impedance controller that regulates a compliant normal force. These results validate the effectiveness of the control strategy and enhance the robot’s adaptability in unstructured human-interaction environments.
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