A Noncontact Control Strategy for Circular Peg-in-Hole Assembly Guided by the 6-DOF Robot Based on Hybrid Vision

Xu, Jingjing; Pei, Yanhu; Yang, Congbin; Liu, Zhifeng

doi:10.1109/tim.2022.3164133

Cited by 16 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work [30] proposes a demonstration system with the optical MoCap sensor where a 6D force-torque sensor is also applied to detect the state of the assembly process. Nonetheless, it works only for cylindrical insertion tasks [38].…”

Section: Rgb Cameramentioning

confidence: 99%

See 1 more Smart Citation

Robot Learning from Demonstration for Assembly with Camera-Supplemented Optical Motion Capture Sensors

Hu,

Yan,

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

Motion capture (MoCap) sensors hold considerable potential in various industrial fields. One of their applications is serving as demonstration data collectors in robot learning from demonstration (LfD) systems. Within these systems, human workers transfer their expertise to robots through demonstrating targeted tasks, thereby providing significant value within the realm of industrial manufacturing. By supplementing the optical MoCap sensors with an RGB camera, a novel assembly demonstration platform and the corresponding policy learning method is proposed here. The 6-dimensional (6D) pose of the parts and video of the human workers' assembly demonstration are simultaneously obtained and temporally aligned. By exploiting both the accuracy and abundance of the demonstration data, the action recognition and parameter estimation method are proposed to learn the policy. The robotic assembly experiments are conducted to validate the proposed demonstration platform and policy learning method. As the experiment results indicate, with the proposed demonstration platform and the policy learning method, the robot can efficiently accomplish precise assembly tasks with a high success rate.

show abstract

Section: Rgb Cameramentioning

confidence: 99%

“…As Fig. 7 exhibits, the target of the aligning policy is guiding the gripped part to fit into the other one with tight clearance, which has been studied as the peg-in-hole problem [3,11,38]. The robot's behavior in the aligning phase mainly affects the success rate of the robotic assembly processes [23,24].…”

Section: Translation Movement Primitivementioning

confidence: 99%

Robot Learning from Demonstration for Assembly with Camera-Supplemented Optical Motion Capture Sensors

Hu,

Yan,

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…At present, robots are widely used in industrial, medical, living and other aspects, of which a considerable number of robots are used in assembly tasks in industry. For the assembly task, Xu et al [1] used a binocular camera to roughly measure the pose of the target hole and then used a monocular camera to improve the image quality to determine the exact pose of the hole. Song et al [2] use training learning and Gaussian strategy to implement the assembly process and use the compliant control method to complete the assembly in the case of uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

“…For the assembly task, Xu et al. [1] used a binocular camera to roughly measure the pose of the target hole and then used a monocular camera to improve the image quality to determine the exact pose of the hole. Song et al.…”

Section: Introductionmentioning

confidence: 99%

Kinematics inverse solution of assembly robot based on improved particle swarm optimization

Zhang,

Li,

Ren

et al. 2024

Robotica

View full text Add to dashboard Cite

Inverse kinematics of robot is the basis of robot assembly, which directly determines the pose of robot. Because the traditional inverse solution algorithm is limited by the robot topology structure, singular pose and inverse solution accuracy, it affects the use of robots. In order to solve the above problems, an improved particle swarm optimization (PSO) algorithm is proposed to solve the inverse problem of robot. This algorithm initializes the particle population based on joint angle limitations, accelerating the convergence speed of the algorithm. In order to avoid falling into local optima and premature convergence, we have proposed a nonlinear weight strategy to update the speed and position of particles, enhancing the algorithm’s search ability, in addition introducing a penalty function to eliminate particles exceeding joint limits. Finally, the positions of common points and singular points are selected on PUMA 560 robot and redundant robot for inverse kinematics simulation verification. The results show that, compared with other algorithms, the improved PSO algorithm has higher convergence accuracy and better convergence speed in solving the inverse solution, and the algorithm has certain universality, which provides a new solution for the inverse kinematics solution of the assembly robot.

show abstract

“…Automatic alignment plays a crucial role in industrial assignments, such as micromanipulation, autonomous welding, and industrial assembly [1][2][3]. Visual servoing is a powerful tool that is commonly used in this application and guides the robotic systems to their desired poses [4][5][6]. The task to be solved by a visual servoing system is to provide velocities of the end-effector that stabilizes and minimizes the difference between image features extracted by a vision device and the desired configurations [7].…”

Section: Introductionmentioning

confidence: 99%

Innovative Adaptive Imaged Based Visual Servoing Control of 6 DoFs Industrial Robot Manipulators

Li,

F. Assadian

2024

Latest Adaptive Control Systems [Working Title]

View full text Add to dashboard Cite

Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.

show abstract

A Noncontact Control Strategy for Circular Peg-in-Hole Assembly Guided by the 6-DOF Robot Based on Hybrid Vision

Cited by 16 publications

References 23 publications

Robot Learning from Demonstration for Assembly with Camera-Supplemented Optical Motion Capture Sensors

Robot Learning from Demonstration for Assembly with Camera-Supplemented Optical Motion Capture Sensors

Kinematics inverse solution of assembly robot based on improved particle swarm optimization

Innovative Adaptive Imaged Based Visual Servoing Control of 6 DoFs Industrial Robot Manipulators

Contact Info

Product

Resources

About