Optimum manipulator path generation based on improved differential evolution constrained optimization algorithm

Abstract

A new improved differential evolution constrained optimization algorithm is proposed to determine the optimum path generation of a rock-drilling manipulator with nine degrees of freedom. This algorithm is developed to minimize the total joint displacement without compromising the pose accuracy of the end-effector. Considering the rule for optimal operation time and smooth joint motion, total joint displacement and minimization of the end-effector pose error are respectively taken as the optimization objective and constraints. In the proposed algorithm, the inverse kinematics solution is computed by self-adaptive mutation differential evolution constrained optimization (SAMDECO) algorithm. Unlike conventional differential evolution (DE) algorithms, in the process of selection operation, the proposed algorithm takes full advantages of the information of excellent infeasible solutions in the contemporary population and scales the contribution of position constraint and orientation constraint. Consequently, the search process is guided to approach the optimal solution from both feasible and infeasible regions, which tremendously improves convergence accuracy and convergence rate. Some contrastive experiments are conducted with the basic self-adaptive mutation differential evoluton (SAMDE) algorithm. The results indicate that the proposed algorithm outperforms the basic SAMDE algorithm in terms of compliance of joints, which raises operation efficiency and plays an important role in engineering services value.

Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms

“…70 Unlike SA algorithm, DE does not have a mechanism which allows it to select a worse solution, nor does it have a mechanism similar to mutation such as can be seen in GA. DE avoids stagnation by having a large search area (configured by parameter F ). 74 A larger value of the parameter F will give a larger search area, but will increase the time it takes for the algorithm to converge to a solution, while the smaller value will cause a faster convergence to a solution, but it will be more likely the found solution will be a local instead of a global optimum. In this research, parameter F is set to 1.2, which is the starting value used in most research.…”

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

“…i is retained to the next generation. A new variant of DE is proposed in Fan, Xie & Zhou (2019). This variant is called self-adaptive mutation differential evolution, which includes a modified version to create the mutant vector v Gþ1 i .…”

“…This new variant of DE proves to be superior than the classical DE algorithm. The self-adaptive mutation differential evolution is called SAMDECO by the authors (Fan, Xie & Zhou, 2019). In this work, SAMDECO is considered to be named as SDE for brevity.…”

A Review and Comparative Study of Differential Evolution Algorithms in Solving Inverse Kinematics of Mobile Manipulator

“…In the literature, researchers have continued to optimize the DE algorithm and have proposed various methods including FADE (Fuzzy Adaptive Differential Evolution) [44], SADE (Self-adaptive Differential Evolution) [45][46][47], JDE [48], JADE [49,50], CODE (composite Differential Evoluton) [51], SAMDE (Self-adaptive Mutation Differential Evoluton) [52], and ODE (Opposition-based Differential Evolution) [53][54][55]. These improvements have mainly focused on crossover operations, mutation operations, mutation factors, and crossover probability.…”