Human body trajectory generation using point cloud data for robotics massage applications

Luo, Ren C.; Chen, Sheng Y.; Yeh, Keng C.

doi:10.1109/icra.2014.6907684

Cited by 14 publications

(3 citation statements)

References 13 publications

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“…Luo R C demonstrated a humanoid two-armed flapping robot in his latest study (Luo and Hsieh, 2018; Luo et al , 2017). Meanwhile, the team used the RANSAC algorithm to estimate the frontal and sagittal planes and generate the specified massage trajectory based on human pose recognition technology (Luo et al , 2014). Dong et al (2022) proposed a flexible jointed parallel massage robot based on series elastic actuators (SEA), contact force stabilization is achieved by controlling the force and position in the surface normal direction without the need for a robot dynamics model.…”

Section: Introductionmentioning

confidence: 99%

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Zhang,

Xiong,

Yin

et al. 2024

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Purpose To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control. Design/methodology/approach First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online. Findings Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment. Originality/value This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

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Section: Introductionmentioning

confidence: 99%

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Zhang,

Xiong,

Yin

et al. 2024

View full text Add to dashboard Cite

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“…When robots are performing massage applications, the end effector of the robot contacts with a certain area (position) of human skin, and contact force are usually required to be controlled at a suitable level (Huang et al , 2014; Luo et al , 2014; Wen et al , 2019; Dometios et al , 2018; Ando et al , 2013). Impedance control, as a compliance control of force and position, has been applied in massage, physiotherapy and other industries (Luo et al , 2016; Luo and Hsieh, 2018; Li et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

An intelligent control system for robot massaging with uncertain skin characteristics

Zhai

Zeng

2022

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Purpose To ensure accurate position and force control of massage robot working on human body with unknown skin characteristics, this study aims to propose a novel intelligent impedance control system. Design/methodology/approach First, a skin dynamic model (SDM) is introduced to describe force-deformation on the human body as feed-forward for force control. Then a particle swarm optimization (PSO) method combined with graph-based knowledge transfer learning (GKT) is studied, which will effectively identify personalized skin parameters. Finally, a self-tuning impedance control strategy is designed to accommodate uncertainty of skin dynamics, system delay and signal noise exist in practical applications. Findings Compared with traditional least square method, genetic algorithm and other kinds of PSO methods, combination of PSO and GKT is validated using experimental data to improve the accuracy and convergence of identification results. The force control is effective, although there are contour errors, control delay and noise problems when the robot does massage on human body. Originality/value Integrating GKT into PSO identification algorithm, and designing an adaptive impedance control algorithm. As a result, the robot can understand textural and biological attributes of its surroundings and adapt its planning activities to carry out a stable and accurate force tracking control during dynamic contacts between a robot and a human.

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“…This robot could recognize the users' back features by pasting color markers on the back [5] [6], but the limitation of this study was treating the human's body as a plane. A study from National Taiwan University used RGB-D cameras to collect the human's back infonnation in 2014 [7], it can effectively reconstruct the human's back and estimate the posture of the human body to realize the feature recognition of the back. However, this study cannot distinguish the details of the back well.…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Robot for Back-Slap Sputum Excretion Based on Back Feature Recognition

Chen

Pan

Huang

et al. 2022

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Human body trajectory generation using point cloud data for robotics massage applications

Cited by 14 publications

References 13 publications

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

Autonomous path planning and stabilizing force interaction control for robotic massage in unknown environment

An intelligent control system for robot massaging with uncertain skin characteristics

Design of an Intelligent Robot for Back-Slap Sputum Excretion Based on Back Feature Recognition

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