Neural-Dynamic-Method-Based Dual-Arm CMG Scheme With Time-Varying Constraints Applied to Humanoid Robots

Abstract

We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

Motion Planning by Demonstration With Human-Likeness Evaluation for Dual-Arm Robots

“…Other related advanced procedures include e.g. the generation of cyclic motions for dual-arm robots using neural networks and quadratic programing [23], the consideration of the robot dynamics in the motion planning by transforming the problem into an optimization of a non-linear fitness function [24] or the use of movement primitives based on a model of the triangle defined by the human upperarm and forearm [25].…”

Analysis, Verification and Comparison on Feedback‐Aided MA Equivalence and Zhang Equivalency of Minimum‐Kinetic‐Energy Type for Kinematic Control of Redundant Robot Manipulators

“…A robot manipulator is the mechanical device that moves its end-effector in order to perform automated tasks [1][2][3][4][5][6][7][8][9][10][11][12][13]. As a special kind of robot manipulator, redundant manipulators are robots having more degrees of freedom (DOFs) than the required ones to execute a given end-effector primary task [14][15][16][17][18][19][20][21][22][23]. They are similar to the human arm, elephant trunk and snake in the real world [17,18,24].…”

General Recurrent Neural Network for Solving Generalized Linear Matrix Equation

“…Different from a number of conventional numerical methods, approaches based on dynamic neural networks can be more realizable on specific parallel and distributed software or/and hardware architectures [17,18]. This could highly enlarge utility of current neural networks towards various potential application domains towards high-performance computing.…”