Study on Redundancy in Robot Kinematic Parameter Identification

Zhang, Yue; Guo, Jingzhe; Li, Xueyan

doi:10.1109/access.2022.3181206

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…Therefore, d 2 is a redundant parameter and can be eliminated. This has been proven in previous studies [27]. Additionally, as described in Section II, a parameter indicating rotation in the y-direction (β i ) was added to solve the singularity problem of axis 2.…”

Section: A System Setupmentioning

confidence: 88%

Relative Positioning Error Minimization of the Dual-Robot System With Kinematic and Base Frame Transformation Parameter Identification

Lee

Min

2023

IEEE Access

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With the increasing use of robots in industry, the use of multi-robot systems, i.e., systems in which multiple robots work together to perform a single task, has become common. In an environment where multiple robots cooperate, the relative position and orientation error between robots must be minimized to improve the accuracy of tasks. In multi-robot systems, accurate kinematic parameters (KP) and base frame transformation (BFT) are required to minimize the position and orientation error between the robots. In this study, a method for KP and BFT identification method that can minimize the position and orientation error of the multi-robot system without using a position measurement device is proposed. A cost function representing the position and orientation error of the multi-robot system was constructed, and the differential evolution algorithm was used to determine the parameters that minimize the cost function. Furthermore, a system consisting of two industrial robots was constructed to verify the effectiveness of the proposed identification method. Experimental results show that the proposed identification method can reduce the relative position error of the dual-robot system by 79% compared to before calibration.

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Section: A System Setupmentioning

confidence: 88%

Relative Positioning Error Minimization of the Dual-Robot System With Kinematic and Base Frame Transformation Parameter Identification

Lee

Min

2023

IEEE Access

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“…This is more in line with the actual motion of the mobile robot. It can greatly improve the performance of the algorithm if combined with other algorithms [32,33].…”

Section: Related Workmentioning

confidence: 99%

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

Sun,

Wang,

et al. 2023

Sensors

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Due to the increased employment of robots in modern society, path planning methods based on human–robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function’s sub-functions and enhance the algorithm’s performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique’s ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

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“…Robot errors arising from geometric parameter errors in the manufacturing process were represented by the improved Denavit–Hartenberg method. [ 9 ] The measurement error of a 3D camera, arising from the baseline, tilt, focal length, and image resolution errors, [ 42 ] has not yet been sufficiently studied. Some studies have used polynomials and Gaussian processes to fit the relationship between the camera error and measured values.…”

Section: Simulationmentioning

confidence: 99%

“…[7] The working process of a 3D robot measurement system is illustrated in Figure 1. Because the workpiece size often exceeds the field of view (FOV) of the 3D camera, the local point clouds of the workpiece are captured from multiple sampled poses, and these point clouds are then transformed into the robot base coordinate system to generate a complete point cloud of the workpiece.However, the measurement accuracy of 3D robot measurement systems is lower than that of CMMs, primarily because of errors related to the robot pose, [8,9] 3D camera installation, [10] and 3D camera measurement. [11,12] If these issues are addressed, a robot measurement system can fully replace CMMs.…”

mentioning

confidence: 99%

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Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

Wu,

Zang,

Bai

et al. 2023

Advanced Intelligent Systems

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Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, , relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

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Study on Redundancy in Robot Kinematic Parameter Identification

Cited by 8 publications

References 32 publications

Relative Positioning Error Minimization of the Dual-Robot System With Kinematic and Base Frame Transformation Parameter Identification

Relative Positioning Error Minimization of the Dual-Robot System With Kinematic and Base Frame Transformation Parameter Identification

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

Simple and High‐Precision Hand–Eye Calibration for 3D Robot Measurement Systems

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