Abstract
To achieve closed trajectory motion planning of redundant manipulators, each joint angle has to be returned to its initial position. Most of the repeatable motion schemes have been proposed to solve kinematic problems considering only the initial desired position of each joint at first. Actually, it is very difficult for various joint angles of the robot arms to be positioned in the expected trajectory before moving. To construct an effective kinematic model, a novel optimal programming index based on a recurrent neural network is designed and analyzed in this paper. The repetitiveness and timeliness are presented and analyzed. Combining the kinematic equation constraint of manipulators, a repeatable motion scheme is formulated. In addition, the Lagrange multiplier theorem is introduced to prove that such a repeatable motion scheme can be converted into a time-varying linear equation. A finite-time neural network solver is constructed for the solution of the motion scheme. Simulation results for two different trajectories illustrate the accuracy and timeliness of the proposed motion scheme. Finally, two different repetitive schemes are compared and verified the optimal time for the novelty of the proposed kinematic scheme.
Highlights
Robot manipulators have been playing an important role in various kinds of engineering fields. ey have been widely used to perform effective and high-intensive repetitive work, such as car assembling, logistics handling, and sculpturing [1,2,3]
Liegeios-Chauvel et al put forward a gradient projection method based on inverse kinematics solution to divide the particular motion controlling into zero space by solving the optimization goal to regulate the solutions for redundancy [8]
Most of the aforementioned approaches for repeatable motion planning of redundant manipulators are effective, the convergent time of the dynamical equations has not been ensured. at is the optimal programming index for motion controlling using neural solvers can make the joint angles of the manipulators back to initial desired position as long as time goes infinity
Summary
Robot manipulators have been playing an important role in various kinds of engineering fields. ey have been widely used to perform effective and high-intensive repetitive work, such as car assembling, logistics handling, and sculpturing [1,2,3]. With the deepening of research studies in repeatable controlling of redundant manipulators, various velocity schemes based on online quadratic optimization have been developed. Simulations on different types of redundant manipulators are studied and different shapes of the trajectory tasks are given, which verified the effectiveness and superiority of the proposed optimal programming index for repeatable motion planning as well as the corresponding neural solvers [16, 17]. At is the optimal programming index for motion controlling using neural solvers can make the joint angles of the manipulators back to initial desired position as long as time goes infinity. Ese models of repetitive motion planning based on pseudo inverse and asymptotic convergent dynamic recurrent neural networks have been studied by many researchers. Comparison results of various repetitive motion schemes are visualized in the end
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
