Abstract
To effectively solve the multi-objective motion planning problem for redundant robot manipulators, a penalty neural multi-criteria optimization (PNMCO) scheme is proposed and investigated. The scheme includes two parts: a constrained multi-criteria optimization (CMCO) subsystem, and a varying-parameter recurrent neural network combined with penalty function (VP-RNN-PF) subsystem. Specifically, the CMCO subsystem is made up of velocity two norm, repetitive motion, and infinity norm. With these criteria, it can achieve energy minimization, repetitive motion, and avoidance of speed peaks. In addition, the CMCO subsystem is then transformed into a standard quadratic programming (QP) problem, and the VP-RNN-PF subsystem is applied to solve the QP problem. Results of computer simulations based on the JACO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> robot manipulator demonstrate that the proposed PNMCO scheme is effective and feasible to plan the multi-objective motion tasks. Comparison experiments of two complex paths tracking between VP-RNN-PF and the traditional neural networks (e.g., simplified linear-variational-inequality-based primal-dual neural network, S-LVI-PDNN) shows that the proposed scheme as well as the neural network is more accurate and more efficient for solving multi-objective motion planning problem.
Highlights
Nowadays, robot technology has received widespread attention and robot manipulators have been widely applied in various fields, such as medical surgery [1], [2], domestic service [3] industrial production [4], [5]
VP-recurrent neural network (RNN)-PF (2.30) and S-LVI-PDNN (2.35) are applied to solve the quadratic programming (QP) problem (2.16)-(2.18) of the constrained multi-criteria optimization (CMCO) scheme, and the robot manipulator is controlled to track the four-leaf-clover path for comparisons
The scheme consists of CMCO and varying parameter RNN (VP-RNN)-PF subsystems
Summary
Robot technology has received widespread attention and robot manipulators have been widely applied in various fields, such as medical surgery [1], [2], domestic service [3] industrial production [4], [5]. The multi-objective motion tasks of redundant robot manipulators with joint physical limits was usually solved by S-LVI-PDNN [8], [16]–[25]. Comparison experiments between VP-RNN-PF and S-LVI-PDNN verify that the proposed schemes more accurate and more efficient for solving the multi-objective motion planning problem.
Sign-in/Register to view highlights & summary 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.
