Research on Robot Surface Tracking Motion Based on Force Control of Six-Axis Wrist Force Sensor

Wang, Zhijun; Liu, Wanyu; Cui, Bo; He, Jing; Li, Zhanxian; Zhao, Yongsheng

doi:10.1155/2014/249696

Cited by 5 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the process of Industry 4.0 continues to advance, force sensors, as key components for real-time detection of force/moment information of mechanical devices, play a crucial role in the field of industrial robotics (Javaid et al, 2021). Compared with traditional single-axis force sensors, the six-axis force/torque sensor (six-axis F/T sensor) can completely capture three-dimensional orthogonal forces and three-dimensional orthogonal torques in any force space system, which has significant advantages and is now widely used in the fields of robotic haptic feedback (Quek et al, 2015), minimally invasive surgery (Kim and Lee, 2016;Yan, 2023) and posture adjustment (Wang et al, 2015;Jia, 2023). According to the elastomer structure, a six-axis F/T sensor can be mainly classified into three types (Templeman et al, 2020): (a) Stewart platform, (b) vertical beam type and (c) beam type.…”

Section: Introductionmentioning

confidence: 99%

Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor

Chen,

Xie,

Wang

et al. 2024

View full text Add to dashboard Cite

Purpose The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor. Design/methodology/approach The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm. Findings The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S. Originality/value Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor

Chen,

Xie,

Wang

et al. 2024

View full text Add to dashboard Cite

show abstract

“…The literature documents three main classifications of workpiece contour acquisition methods, including 3D Scan/CAD data, surface tracking techniques using force sensors, and image processing. Wang et al [11] used the workpiece's CAD data to realize the actual shape of surfaces. Another commonly used approach for acquiring surface paths is 3D scanning; however, this approach ignores fixture uncertainty, and it is typically challenging to match the part's surface to that of the CAD model because the aforementioned approach does not account for the part's orientation.…”

Section: Introductionmentioning

confidence: 99%

“…Another commonly used approach for acquiring surface paths is 3D scanning; however, this approach ignores fixture uncertainty, and it is typically challenging to match the part's surface to that of the CAD model because the aforementioned approach does not account for the part's orientation. On the other hand, a contact-based method that estimates normal contact with the surface by utilizing the feedback information from a force/torque sensor has been documented in the literature [11][12][13][14]. The obtained force information is fed back into the algorithm to estimate the desired angle for acquiring a perpendicular relationship between the robot's end-effector and the contact surface.…”

Section: Introductionmentioning

confidence: 99%

Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach

Shah

Lin

Tran

et al. 2023

Sensors

View full text Add to dashboard Cite

The use of robots for machining operations has become very popular in the last few decades. However, the challenge of the robotic-based machining process, such as surface finishing on curved surfaces, still persists. Prior studies (non-contact- and contact-based) have their own limitations, such as fixture error and surface friction. To cope with these challenges, this study proposes an advanced technique for path correction and normal trajectory generation while tracking a curved workpiece’s surface. Initially, a key-point selection approach is used to estimate a reference workpiece’s coordinates using a depth measuring tool. This approach overcomes the fixture errors and enables the robot to track the desired path, i.e., where the surface normal trajectory is needed. Subsequently, this study employs an attached RGB-D camera on the end-effector of the robot for determining the depth and angle between the robot and the contact surface, which nullifies surface friction issues. The point cloud information of the contact surface is employed by the pose correction algorithm to guarantee the robot’s perpendicularity and constant contact with the surface. The efficiency of the proposed technique is analyzed by carrying out several experimental trials using a 6 DOF robot manipulator. The results reveal a better normal trajectory generation than previous state-of-the-art research, with an average angle and depth error of 1.8 degrees and 0.4 mm.

show abstract

“…Using intrinsic contact sensing, Sun and Liu (2020) proposed a force and velocity algorithm that automatically explores the surface of dynamic objects. Some similar force control algorithms (Visioli et al , 2010; Winkler and Suchý, 2014; Wang et al , 2015; Wu et al , 2009) can easily explore the workpiece contours through force sensors. It should be pointed out that to ensure the consistency of the grinding surface of the workpiece and the stable contact state between the robot and the workpiece, constant force tracking of the robot is very important (Ziliani et al , 2008).…”

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

View full text Add to dashboard Cite

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.

show abstract

Research on Robot Surface Tracking Motion Based on Force Control of Six-Axis Wrist Force Sensor

Cited by 5 publications

References 21 publications

Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor

Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor

Robot Pose Estimation and Normal Trajectory Generation on Curved Surface Using an Enhanced Non-Contact Approach

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

Contact Info

Product

Resources

About