scholarly journals Sorting path optimization of parallel robot based on improved Genetic Algorithm

2021 ◽  
Vol 2121 (1) ◽  
pp. 012018
Author(s):  
Feize Xia ◽  
Yuan Sun ◽  
Meng Wang
Keyword(s):  
Genetic Algorithm ◽  
Parallel Robot ◽  
Motion Path ◽  
Improved Genetic Algorithm ◽  
End Effector ◽  
Total Distance ◽  
Sorting Time ◽  
Sorting Problems ◽  
Increase Productivity ◽  
Sorting Efficiency

Abstract In industrial production, parallel robot is often used for sorting, battery clamp is divided into two structures, the classification and sorting, for multi-target sorting problems. The motion path of end-effector is planned, the overall sorting path is planned, and the practical problem is transformed into a similar traveling salesman problem. The improved genetic algorithm is proposed to optimize the sorting time sequence, and the optimized path shortens 10.23% total distance on average compared with random sorting and 5.37% total distance compared with fixed longitudinal sorting. The shortening of the total distance can lead to higher sorting efficiency in sorting and increase productivity.

Three-Degree-of-Freedom Parallel Robot Arm

2006 ◽  
Author(s):  
Martin Hosek ◽  
Michael Valasek ◽  
Jairo Moura
Keyword(s):  
Parallel Robot ◽  
Degree Of Freedom ◽  
Motion Path ◽  
Angular Orientation ◽  
Robot Arm ◽  
Flat Panel Display ◽  
End Effector ◽  
Cluster Tools ◽  
Manufacturing Applications ◽  
Three Degree Of Freedom

This paper presents single- and dual-end-effector configurations of a planar three-degree of freedom parallel robot arm designed for automated pick-place operations in vacuum cluster tools for semiconductor and flat-panel-display manufacturing applications. The basic single end-effector configuration of the arm consists of a pivoting base platform, two elbow platforms and a wrist platform, which are connected through two symmetric pairs of parallelogram mechanisms. The wrist platform carries an end-effector, the position and angular orientation of which can be controlled independently by three motors located at the base of the robot. The joints and links of the mechanism are arranged in a unique geometric configuration which provides a sufficient range of motion for typical vacuum cluster tools. The geometric properties of the mechanism are further optimized for a given motion path of the robot. In addition to the basic symmetric single end-effector configuration, an asymmetric costeffective version of the mechanism is derived, and two dual-end-effector alternatives for improved throughput performance are described. In contrast to prior attempts to control angular orientation of the end-effector(s) of the conventional arms employed currently in vacuum cluster tools, all of the motors that drive the arm can be located at the stationary base of the robot with no need for joint actuators carried by the arm or complicated belt arrangements running through the arm. As a result, the motors do not contribute to the mass and inertia properties of the moving parts of the arm, no power and signal wires through the arm are necessary, the reliability and maintenance aspects of operation are improved, and the level of undesirable particle generation is reduced. This is particularly beneficial for high-throughput applications in vacuum and particlesensitive environments.

scholarly journals Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141879299 ◽  
Author(s):  
Zhiyu Zhou ◽  
Hanxuan Guo ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Inverse Kinematics ◽  
Robotic Manipulator ◽  
Computational Time ◽  
Improved Genetic Algorithm ◽  
Intelligent Algorithm ◽  
End Effector ◽  
Training Time ◽  
Learning Machine

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

Generate optimal grasping trajectories to the end-effector using an improved genetic algorithm

2020 ◽  
Vol 66 (7) ◽  
pp. 1803-1817
Author(s):  
Mingming Wang ◽  
Jianjun Luo ◽  
Lili Zheng ◽  
Jianping Yuan ◽  
Ulrich Walter
Keyword(s):  
Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
End Effector

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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