Genetic algorithm based optimization for Dual-Arm Cam-Lock robot configuration

2007 ◽  
Author(s):  
Kambiz Ghaemi Osgouie ◽  
Ali Meghdari ◽  
Saeed Sohrabpour ◽  
Mehdi Salmani Jelodar
Keyword(s):  
Genetic Algorithm ◽  
Robot Configuration ◽  
Dual Arm

scholarly journals Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hui Zhang ◽  
Wenbin Zha ◽  
Xiangrong Xu ◽  
Yongfei Zhu
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Optimization ◽  
Control Method ◽  
Time Varying ◽  
Pd Control ◽  
Output State ◽  
Optimization Control ◽  
The Individual ◽  
The Impact ◽  
Dual Arm

Aiming at the impact and disturbance of dual-arm robots in the process of coordinated transportation, a dual-arm cooperative trajectory optimization control based on time-varying constrained output state is proposed. According to the constraint relationship of the end-effector trajectory of the dual-arm coordinated transportation, the joint space trajectory mathematical model of the dual-arm coordinated transportation was established by using the master-slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B-spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time-varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double-arm handling experiment.

scholarly journals Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2355 ◽  
Author(s):  
Tan Zhang ◽  
Wing-Yue Louie ◽  
Goldie Nejat ◽  
Beno Benhabib
Keyword(s):  
Social Communication ◽  
Learning System ◽  
Imitation Learning ◽  
High Dimensionality ◽  
Human Behaviors ◽  
Bounding Volumes ◽  
Communication Modes ◽  
Robot Configuration ◽  
Kinematical Approach ◽  
Dual Arm

To effectively interact with people, social robots need to perceive human behaviors and in turn display their own behaviors using social communication modes such as gestures. The modeling of gestures can be difficult due to the high dimensionality of the robot configuration space. Imitation learning can be used to teach a robot to implement multi-jointed arm gestures by directly observing a human teacher’s arm movements (for example, using a non-contact 3D sensor) and then mapping these movements onto the robot arms. In this paper, we present a novel imitation learning system with robot self-collision awareness and avoidance. The proposed method uses a kinematical approach with bounding volumes to detect and avoid collisions with the robot itself while performing gesticulations. We conducted experiments with a dual arm social robot and a 3D sensor to determine the effectiveness of our imitation system in being able to mimic gestures while avoiding self-collisions.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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