Online teaching of robotic arm by human–robot interaction: end effector force/torque sensing

Almusawi, Ahmed R. J.; Dülger, L. Canan; Kapucu, Sadettin

doi:10.1007/s40430-018-1358-3

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…In the existing research, the force sensing model of the robot end is usually established by analytical method, that is, considering the physical relationship between the factors affecting the force sensing, the mathematical model between the contact force and known quantities is derived [14][15][16]. However, there are many factors that affect the force sensing, including the installation angle of the robot base, the installation angle of the sensor on the robot, the position and pose feedback error of the robot, etc.…”

Section: Figure 1 Schematic Diagram Of the Force Acting On The Loadmentioning

confidence: 99%

Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Meng

Zhang

et al. 2021

Preprint

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Towards the zero-gravity simulation test requirements of spacecraft for on-orbit service missions, the zero-gravity motion simulation technology based on industrial robot was studied. In order to realize the accurate force sensing of load on robot, the BP neural network was used to establish the force prediction model. The model uses the robot pose, acceleration, angular velocity, and angular acceleration as input layer parameters, and uses the data from force/torque sensor on robot wrist as the output layer parameter. In order to realize the accurate force perception in whole working space of the robot, the orthogonal experiment design method was adopted to determine the path points of the robot for sample data collection. Based on the established force prediction model, the accurate force sensing of the end load of the robot under moving conditions is realized. In experiment, for the load with 100kg mass on robot, the maximum error of force sensing is 39.8N, and the root mean square error of force sensing is 9.3N. The maximum error of torque sensing is 18.7Nm, and the root mean square error of torque sensing is 4.4Nm. Furthermore, according to the real-time force sensing results of the robot load, the motion speed of the load in zero-gravity state is calculated by dynamics theory. Then the robot is driven to execute the corresponding motion of the load, and the zero gravity motion simulation of the load is realized.

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Section: Figure 1 Schematic Diagram Of the Force Acting On The Loadmentioning

confidence: 99%

Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Meng

Zhang

et al. 2021

Preprint

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“…By engaging in interdisciplinary projects, students learn effective communication, collaboration, and appreciation for diverse perspectives, preparing them for success in interdisciplinary teamwork both academically and professionally. Additionally, the interdisciplinary project-integrated approach promotes adaptability and flexibility by equipping students with versatile skills that enable them to navigate the rapidly evolving landscape of robotics technology [6]. With proficiency in multiple disciplines, students become adept problem-solvers who are ready to tackle the dynamic challenges of the field, contributing to the advancement of robotics technology and shaping the future of the industry.…”

Section: Introductionmentioning

confidence: 99%

Interdisciplinary project-integrated teaching method in control and drive courses for robotics engineering oriented students

2024

avte

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Robot engineering education plays an important role in shaping the future of automation and robotics. Among the core subjects, control systems and drive technologies stand as fundamental pillars, demanding innovative teaching methodologies to ensure comprehensive learning outcomes. This paper investigates the efficacy of interdisciplinary approach in enhancing student understanding and practical application of control and drive concepts in robot engineering courses. It highlights the implementation methods such as integrated projects and laboratory sessions, as well as other techniques including projectbased learning. Through a comparative analysis of these methods, key factors influencing student engagement, knowledge retention, and skill development can be identified. This paper also explores the benefits and the real-world application of this method of teaching, highlighting its potential to revolutionize the teaching of control and drive courses. By synthesizing insights from existing literature and empirical studies, this research offers a valuable recommendation for educators seeking to optimize teaching strategies in robot engineering education.

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“…As a way of human–robot collaboration, direct teaching (Ren et al , 2019) enables the coworkers to easily guide robots to perform grinding tasks through 3D Mouse (Xiao et al , 2021), force sensors (Almusawi et al , 2018), gesture sensors (Liu and Wang, 2018), force observer (Xiao et al , 2019) and feedforward control (Wu et al , 2021). But human–robot collaboration in toolpath generation could introduce position and orientation errors to toolpaths.…”

Section: Introductionmentioning

confidence: 99%

Robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration

Zhao

Xiao

Liu

et al. 2022

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Purpose In customized production such as plate workpiece grinding, because of the diversity of the workpiece shapes and the positional/orientational clamping errors, great efforts are taken to repeatedly calibrate and program the robots. To change this situation, the purpose of this study is to propose a method of robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration. Design/methodology/approach First, an adaptive constant force controller based on stiffness estimation is proposed, which can distinguish the contact of the human hand and the unknown workpiece contour. Second, a normal vector search algorithm is developed to calculate the normal vector of the unknown workpiece contour in real-time. Finally, the force and position are controlled in the calculated normal and tangential directions to realize the direct grinding. Findings The method considers the disturbance of the tangential grinding force and the friction, so the robot can track and grind the workpiece contour simultaneously. The experiments prove that the method can ensure the force error and the normal vector calculating error within 0.3 N and 4°. This human–robot collaboration pattern improves the convenience of the grinding process. Research limitations/implications The proposed method realizes constant force grinding of unknown workpiece contour in real-time and ensures the grinding consistency. In addition, combined with human–robot collaboration, the method saves the time spent in repeated calibration and programming. Originality/value Compared with other related research, this method has better accuracy and anti-disturbance capability of force control and normal vector calculation during the actual grinding process.

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Online teaching of robotic arm by human–robot interaction: end effector force/torque sensing

Cited by 12 publications

References 14 publications

Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Interdisciplinary project-integrated teaching method in control and drive courses for robotics engineering oriented students

Robotic direct grinding for unknown workpiece contour based on adaptive constant force control and human–robot collaboration

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