Structural kinematics of 6-revolute-axis robot manipulators

Manseur, Rachid; Doty, Keith L.

doi:10.1016/0094-114x(95)00092-d

Cited by 25 publications

(12 citation statements)

References 15 publications

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“…At present, four methods are always applied to solve the inverse kinematics problem of the redundant robot finger. They are geometric method, algebraic method, numerical iteration method and artificial intelligent method [15]. Geometric method suffers from a lack of generality; algebraic method is only suitable for the robot finger with a few joints; Numerical iteration method can get one solution derived from a variety of iterative solutions, but it is difficult to guarantee the correctness of solutions; artificial intelligent An improved adaptive genetic algorithm (IAGA) is applied to solve the inverse kinematics problem of the finger in this work.…”

Section: Inverse Kinematics Of Fingermentioning

confidence: 99%

Design and control of integrated pneumatic dexterous robot finger

Wang

Zhang

Bao

et al. 2011

J. Cent. South Univ. Technol.

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Based on flexible pneumatic actuator (FPA), bending joint and side-sway joint, a new kind of pneumatic dexterous robot finger was developed. The finger is equipped with one five-component force sensor and four contactless magnetic rotary encoders. Mechanical parts and FPAs are integrated, which reduces the overall size of the finger. Driven by FPA directly, the joint output torque is more accurate and the friction and vibration can be effectively reduced. An improved adaptive genetic algorithm (IAGA) was adopted to solve the inverse kinematics problem of the redundant finger. The statics of the finger was analyzed and the relation between fingertip force and joint torque was built. Finally, the finger force/position control principle was introduced. Tracking experiments of fingertip force/position were carried out. The experimental results show that the fingertip position tracking error is within ±1 mm and the fingertip force tracking error is within ±0.4 N. It is also concluded from the theoretical and experimental results that the finger can be controlled and it has a good application prospect.

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Section: Inverse Kinematics Of Fingermentioning

confidence: 99%

Design and control of integrated pneumatic dexterous robot finger

Wang

Zhang

Bao

et al. 2011

J. Cent. South Univ. Technol.

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show abstract

“…Currently, the general method of solving the inverse kinematics is based on the means of inverse kinematics equations in which the analytical solution of each joint variable is obtained. This method fits most circumstances [1][2][3]. However, when no analytic solution exist or endless solutions exist in limited error, it is difficult, even impossible to solve the inverse kinematics equations by using the general method.…”

Section: Introductionmentioning

confidence: 99%

An adaptive PSO-based method for inverse kinematics analysis of serial manipulator

Zhang¹,

Mu²,

Ma³

et al. 2012

2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering

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The Adaptive Particle Swarm Optimization method(A-PSO) combined the kinematic equations is proposed in this paper. The A-PSO is capable of solving the optimal solution of joint variables. The inverse kinematics mathematic model of the serial dangerous articles disposal manipulator with multiple degrees of freedom (Multi-DOFs) is built. In the proposed searching process, the position matrix and the pose matrix of the fitness function are adjusted by joints form, the steps of the particle swarm change with the global extremum. The proposed method is able to reduce the complexity of the analysis of inverse kinematic equations, and the adaptive solution can be obtained. Compared to the traditional PSO algorithm, the proposed A-PSO can obtain the logical joint variables in a more computationally efficient manner. The accuracy and efficiency of the proposed method is demonstrated in the case study.

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“…In the process of solving this problem, the inverse matrix and the computation of the anti-trigonometric function must be computed with the methods in Refs. [1][2][3][4][5][6][7][8], and the quantity of computation task increases in n exponent power along with the increase of robot' s joints number (n).…”

Section: Introductionmentioning

confidence: 99%

Trial mountain climbing algorithm for solving the inverse kinematics of redundant manipulator

Zhou

Deng

2002

J Cent. South Univ. Technol.

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Trial mountain climbing algorithm to solve the inverse kinematics problem of redundant manipulator is introduced, and a method of describing a numeral with a special numeration system is given to define the changed step of the trail mountain climbing algorithm. The results show that a likelihood solution can be found quickly in the infinite groups of likelihood solutions within the limited search times, and need not calculate the anti-trigonometric function and the inverse matrix. In addition, this algorithm has many good qualities such as concise algorithm, tiny computation, fast convergence velocity, good stability and extensive adaptability.

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Structural kinematics of 6-revolute-axis robot manipulators

Cited by 25 publications

References 15 publications

Design and control of integrated pneumatic dexterous robot finger

Design and control of integrated pneumatic dexterous robot finger

An adaptive PSO-based method for inverse kinematics analysis of serial manipulator

Trial mountain climbing algorithm for solving the inverse kinematics of redundant manipulator

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