A Multi-objective Differential Evolution Algorithm for Robot Inverse Kinematics

Buendía-Rodríguez, Enrique; Cimat,; Saha, Baidya Nath; Romero-Hdz, Jesus; González-Ortega, D.

doi:10.14445/23488387/ijcse-v3i11p113

Cited by 4 publications

(4 citation statements)

References 14 publications

(22 reference statements)

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“…• Zakaryan et al [58] used a weight-sum-based DE [59] to retrieve the optimal link and joint weights that minimize (i) the total mass of the device, (ii) the maximal magnitudes of cable tensions, and (iii) the maximal difference between magnitudes of agonist-antagonist cable tensions (i.e., to resemble the structure of the natural muscular system of human limbs) of their arm exoskeleton for rehabilitation.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey

Stroppa,

Soylemez,

Yuksel

et al. 2023

Robotics

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Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human–machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons. We completed the search in the IEEE, ACM, and MDPI databases between 2017 and 2023 using the keywords “exoskeleton”, “design”, and “optimization”. We categorized our findings in terms of which limb (i.e., hand, wrist, arm, or leg) and application (assistive, rehabilitation, or haptic) the exoskeleton was designed for, the optimization metrics (force transmission, workspace, size, and adjustability/calibration), and the optimization method (categorized as evolutionary computation or non-evolutionary computation methods). We discuss our observations with respect to how the optimization methods have been implemented based on our findings. We conclude our paper with suggestions for future research.

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Section: Differential Evolution (De)mentioning

confidence: 99%

Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey

Stroppa,

Soylemez,

Yuksel

et al. 2023

Robotics

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show abstract

“…algorithm and apply the DE algorithm to a mobile manipulator robot and compare the performance when tracking different trajectories, which show that the DE algorithm has many advantages in solving the IK of mobile manipulatosr. Compared with the traditional method, the DE algorithm [41] is applied to the collaborative task of multimobile manipulator robots. The method could effectively solve the problem by redefining the constraint conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Qiao

2023

Symmetry

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Mobile manipulator robots have become important pieces of equipment due to the high mobility of mobile subsystems and the high flexibility of manipulator subsystems. Considering the increasing degrees of freedom and the need to avoid singular locations, one of the most challenging problems is solving the inverse kinematics problem of mobile manipulator robots (IKMM). Of all the popular optimization algorithms, the differential evolution (DE) algorithm is the most effective method for quickly solving the IKMM problem with sufficient solutions. Currently, many strategies have been proposed for DE algorithms to improve the performance of solving mathematical problems; some symmetry strategies or symmetry functions have been introduced to DE algorithms. However, the effects of various DE algorithms on solving the actual IKMM lack a comprehensive explanation. Therefore, we divide various DE algorithms into three categories considering the control parameter selection and compare the specific optimization of various DE algorithms. Then, we compare the performance of various DE algorithms when solving the inverse kinematics problems of mobile manipulators with different degrees of freedom. Considering the effectiveness and the speed of the DE algorithm on the IKMM problem, we determine the best DE algorithm by comparing the error and time required to reach 100 random mission points and tracking the typical trajectories. Finally, the best-performing DE method is further improved by studying the selection of fundamental parameters in the best DE algorithm. Valuable conclusions are obtained from these experimental simulations, which can help with choosing an algorithm that is suitable for solving the inverse kinematics problem of mobile manipulator robots in practice.

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“…Different solution techniques for inverse kinematics can be categorized into two major classes: closed-form analytical and numerical methods. [4][5][6][7] Each method has its own advantages and disadvantages. Closed-form methods involve solving complex transcendental equations to obtain a set of joint angles, which are applicable to models with three joint axes having a common intersection.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, inverse kinematics of manipulator with the objective of "optimal operation time" 12 and "best compliance," 6 that is, a smooth joint motion 1 has become a research hot spot for scholars at home and abroad. Weighted sum method 1,4 has been especially used to solve the inverse kinematics problem associated with the manipulator optimal path generation, which transforms the pose accuracy and best compliance into an optimization function by linear weighting. Afterward, metaheuristic algorithms are employed to solve the optimal solution of the function.…”

Section: Introductionmentioning

confidence: 99%

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

Fan

Xie

Zhou

2019

International Journal of Advanced Robotic Systems

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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.

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A Multi-objective Differential Evolution Algorithm for Robot Inverse Kinematics

Cited by 4 publications

References 14 publications

Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey

Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey

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

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

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