Step-by-step identification of industrial robot dynamics model parameters and force-free control for robot teaching

Binrui Wang,Junwei Fang,Shunan Qi,Ling Wang,Xiaolong Liu,Haijun Ren 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7

In order to solve the problem of lack of flexibility in direct teaching of industrial robots under complex working conditions, dynamic inertia term compensation is combined with the traditional force-free control algorithm to reduce the traction in the teaching process in this paper. Firstly, considering the multi-dimensional, strongly nonlinear and multi-parameter characteristics of the 6-DOF manipulator dynamic model, a step-by-step parameters identification method based on genetic algorithm is proposed. This method can effectively reduce the computational complexity and improve the optimization speed of industrial robot dynamic model identification. Secondly, based on the internal torque analysis of industrial robot joints, a forcefree compensation control algorithm based on the torque control is designed. Finally, the effectiveness of the step-by-step robot dynamic model parameter identification method is verified by a traction teaching experiment. In the verification experiment, the relative error rate between the predicted torque and the actual torque calculated from the identification results is 1.64 % to 5.99 %. After increasing the inertia term compensation, the traction force of the teaching process is reduced by 28.6 %. The experimental results show that the identification result is more accurate, and the proposed force-free control algorithm significantly improves the compliance of the teaching process.

Dynamic optimization of robot arm based on flexible multi-body model

Mingxuan Liang,Binrui Wang,Tianhong Yan 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.8

This paper examines the effect mechanism of torsional stiffness on flexible joints and the dynamic optimization of a six Degree-offreedom industrial robot arm. The design optimization of the robot arm is investigated based on the rotor-torsional spring model and finite element method. The flexible multi-body dynamic model of the robot arm are established by considering the flexible characteristics of arms and joints, and the natural frequencies of a robot arm are calculated to obtain the torsional stiffness of the flexible joints. Natural frequency results gradually increased with joint stiffness improvement. Using the established dynamic model, the topology optimization on the robot arm is carried out by regarding lightweight as design goal and total displacement as constraints. The tare-load ratio and dynamic performance of the optimized robot arm are significantly enhanced compared with the original design model. This research can provide the theoretical basis for the dynamic optimization and upgrade of lightweight robot arm.

Human-robot Collision Detection Based on the Improved Camshift Algorithm and Bounding Box

Shuangning Lu,Zhouda Xu,Binrui Wang 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.10

Aiming at the problem of collision detection and collision point information evaluation in the process of human-robot collaboration, the binocular camera is used as an external sensor to observe. Collision detection is realized by tracking the motion of human-robot through the color information of joints. Firstly, the Camshift algorithm is used to track the position of the manipulator joints and the human arm joints based on the color information. In order to solve the factors that may cause target loss during tracking process, such as shelter and background color similar problems, Kalman filter is integrated on the basis of Camshift algorithm. A similarity threshold is set to judge whether there is interference in the tracking process. The tracking experiment proved that the Kalman filter is effective and enhances the robustness of the tracking algorithm. Secondly, a bounding box collision detection method based on space domain is designed. The sphere bounding box and the cylindrical bounding box is used as the human-robot bounding boxes. The equations of the distance between different boxes are derived and the position of the collision point on the manipulator is calculated. Finally, an experimental environment is built for verification. The distance error of the collision is within 0-10 mm, and the position error between the calculated collision point and the pre-determined collision point is within 10%.

Recognition Algorithm and Optimization Experiments on Tomato Picking Robots

Xifeng Liang,Zhengshuai Jiang,Binrui Wang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.9

In order to improve the recognition accuracy of vision system on tomato picking robots, the paper proposed a method of feature extraction and recognition for ripe tomato based on illumination irrelevant images and support vector machine (SVM). In this method, we adopted vector median filter (VMF) to process the tomato images to eliminate noise and make the images more smooth firstly. To avoid the effects of natural environment illumination to the vision system, we processed tomato images and obtained the tomato illumination irrelevant images according to color constancy algorithm of the single pixel. Secondly, we segmented illumination irrelevant images using OSTU method, separated multiple objects by a watershed algorithm based on distance transform and got the target area with mathematical morphology. Also we extracted color, shape and textural features of the ripe tomatoes. Finally, we did experiments on recognizing tomatoes using support vector machine (SVM) with different kernel functions. At the same time, in order to obtain optimal model of SVM, we adopted cross validation and grid search method to optimize the model parameters. The experiment results show that illumination irrelevant processing not only can eliminate the influence of light intensity, but save a gray transferring step for further image segmentation. SVM with radial basis function is better than other kernel functions SVM and the tomato recognition accuracy is 95.7%. Through optimizing the parameter C and r of radial basis function, the tomato recognition accuracy reaches up to 96.9% with the increase of 1.2% when C and r is 4 and 16 respectively. This proves that it's feasible and effective to optimize SVM's parameters by cross validation and grid search method, which provide foundation for further research on vision system of tomato picking robots.

Design of Adaptive RBFNN and Computed-torque Control for Manipulator Joint Considering Friction Modeling

Xiaobin Shen,Kun Zhou,Rui Yu,Binrui Wang 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.7

In this paper, we aim to improve the tracking performance of the manipulator joint system by establishing accurate friction model based on the Stribeck model and the cubic polynomial method. Meanwhile, in view of the established system model, an adaptive Radial Basis Function Neural Network (RBFNN) compensation computedtorque controller is designed for the manipulator joint system. Firstly, we consider the friction modeling process at low- and high- velocity regions to advance the model accuracy, and identify the parameters in the friction model equation offline via the particle swarm optimization (PSO) algorithm. Secondly, an adaptive RBFNN algorithm is developed to analyze the unmodeled dynamics online and introduce it to the computed-torque controller design. After that, we further conduct the stability analysis for the proposed controller based on the Lyapunov stability criterion. Finally, the self-developed manipulator joint platform introduction, the simulation experiment and the contradistinctive experiments are given to illustrate the effectiveness of designed controller.

An Improved Dynamic Window Path Planning Algorithm Using Multi-algorithm Fusion

Rui Zhou,Kun Zhou,Lina Wang,Binrui Wang 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3

Aiming at the problems of poor adaptability of traditional dynamic window algorithms and difficult toquickly and effectively plan paths in the face of complex obstacles such as spiral obstacles and narrow obstacles,we propose an improved dynamic windows approach path planning algorithm based on A* algorithm and artificialpotential field method fusion. Firstly, we improve the security constraints of the dynamic window algorithm, replace the obstacle distance evaluation function in the original algorithm with the artificial repulsion field function,and add the target endpoint distance sub-evaluation function. Secondly, the improved dynamic window method isintegrated with the A* path smoothed by gradient descent method, which solves the problem of poor global planning of the traditional algorithm. And the weight of the evaluation function will adaptively change according tothe surrounding environment, which enhanced the adaptability of the algorithm. Finally, through the comparison ofsimulation results, we verified that the fusion algorithm has a great improvement in planning efficiency, safety, andpath smoothness, and is more in line with the motion characteristics of mobile robots.