Feedback-Added Pseudoinverse-Type Balanced Minimization Scheme for Kinematic Control of Redundant Robot Manipulators

Wang, Zhenhuan; Wang, Boyang; Xu, Lingjie; Xie, Qinyu

doi:10.1109/access.2019.2899587

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…[6][7][8] Many related research papers have been published. 1,2,6,[9][10][11] The singular value decomposition generalized inverse method (SGIM) is a classical and common scheme to obtain inverse kinematics solutions for redundant manipulators, which provides a well-defined Jacobian generalized inverse matrix by singular value decomposition. 5,9,12 Although the least norm under least-squares solutions can be easily obtained by the SGIM, 9,12 the practicality of the solution will suffer, because the norm of the angular velocity tends to infinity when the least singular value approaches zero.…”

Section: Introductionmentioning

confidence: 99%

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Improved singular robust inverse solutions of redundant serial manipulators

Sun

Liu

Chen

2020

International Journal of Advanced Robotic Systems

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To address the Jacobian matrix approximation error, which usually exists in the iterative solving process of the classic singular robust inverse method, the correction coefficient α is introduced, and the improved singular robust inverse method is the result. On this basis, the constant improved singular robust method and the intelligent improved singular robust inverse method are proposed. In addition, a new scheme, combining particle swarm optimization and artificial neural network training, is applied to obtain real-time parameters. The stability of the proposed methods is verified according to the Lyapunov stability criteria, and the effectiveness is verified in the application examples of spatial linear and curve trajectories with a seven-axis manipulator. The simulation results show that the improved singular robust inverse method has better optimization performance and stability. In the allowable range, the terminal error is smallest, and there is no lasting oscillation or large amplitude. The least singular value is largest, and the joint angular velocity is smallest, exactly as expected. The derivative of the Lyapunov function is negative definite. Comparing the two extended methods, the constant improved singular robust method performs better in terms of joint angular velocity and least singular value optimization, and the intelligent improved singular robust inverse method can achieve a smaller terminal error. There is little difference between their overall optimization effects. However, the adaptability of the real-time parameters makes the intelligent improved singular robust inverse method the first choice for kinematic control of redundant serial manipulators.

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Section: Introductionmentioning

confidence: 99%

“…Kinematics is the basis of manipulator control technology, which focuses on inverse kinematics solutions. 1 –5 However, due to the high nonlinearity of redundant manipulators, it is quite difficult to obtain a suitable solution. The classic method is used to obtain the pseudoinverse solutions of various target functions.…”

Section: Introductionmentioning

confidence: 99%

Improved singular robust inverse solutions of redundant serial manipulators

Sun

Liu

Chen

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…There have been many studies on classical methods used to find the pseudoinverse solution of various target functions. 2,3,6,[9][10][11][12][13] The singular value decomposition generalized inverse method (SGIM) is a typical and widely used method for solving inverse motion of redundant manipulators, and it provides a defined Jacobian generalized inverse by singular value decomposition. 8,10,14,15 The optimization object of SGIM is the least squares solution with the minimum norm.…”

Section: Introductionmentioning

confidence: 99%

“…Kinematics control is one of the most general issues in robot manipulator technology, in which the focus is on the inverse kinematics solution. 1–8 However, it is often difficult to find a suitable solution for redundant manipulators due to high nonlinearity. There have been many studies on classical methods used to find the pseudoinverse solution of various target functions.…”

Section: Introductionmentioning

confidence: 99%

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Extended singular robust inverse solution of redundant serial manipulators

Sun

Liu

Chen

2020

Advances in Mechanical Engineering

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We propose the use of a damping matrix β to replace the unified damping coefficient λ in the singular robust inverse method, allowing for adjustment of the manipulator’s singularity for different joints separately. In addition, the correction coefficient matrix α is employed to eliminate the Jacobian matrix approximation error, which usually exists in the iterative control solving process; the extended singular robust inverse method is thereby proposed. This article contains analyses of the singularities caused by three methods and how they result in larger values of the manipulator terminal error and joint angular velocity; additionally, the semi-empirical selection principles of β and α are given. The stability of the proposed method is investigated with the Lyapunov stability criteria, and its effectiveness is verified for spatial linear and curve trajectories with 7-axis and 10-axis redundant serial manipulators. The simulation results show that the proposed method exhibits improved optimization performance and stability, and the terminal error is stable and within the allowable range. The least singular value is larger and the joint angular velocity is the lowest, which is the expected outcome. Furthermore, the simulation results for the 10-axis manipulator also indicate the generality of the proposed method.

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