Trajectory Generation for Redundant Manipulator using Virus-Evolutionary Genetic Algorithm with Subpopulations

1997 ◽  
Vol 1 (2) ◽  
pp. 155-161
Author(s):  
Takemasa Arakawa ◽  
◽  
Toshio Fukuda ◽  
Naoyuki Kubota ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Information ◽  
Energy Optimization ◽  
Trajectory Generation ◽  
Redundant Manipulators ◽  
Redundant Manipulator ◽  
Theory Of Evolution ◽  
Evolutionary Genetic ◽  
Generation Problem ◽  
Virus Theory

In this paper, we apply a virus-evolutionary genetic algorithm with subpopulations (VEGAS) to a trajectory generation problem for redundant manipulators through energy optimization. VEGAS is based on the virus theory of evolution and VEGAS has some subpopulations that usually evolve independently. In the same subpopulation, a virus infects host individuals. And a virus sometimes immigrates from one subpopulation to another. The genetic information from one subpopulation propagates in another subpopulation only through immigration of the virus. The energy-optimized collision-free trajectory was found successfully using VEGAS.

A new motion planning method for discretely actuated hyper-redundant manipulators

Robotica ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 101-118 ◽  
Author(s):  
Alireza Motahari ◽  
Hassan Zohoor ◽  
Moharam Habibnejad Korayem
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Trajectory Tracking ◽  
Inverse Kinematic ◽  
Planning Problem ◽  
Redundant Manipulators ◽  
Tracking Problem ◽  
Parallel Mechanisms ◽  
Redundant Manipulator ◽  
The Common

SUMMARYA hyper-redundant manipulator is made by mounting the serial and/or parallel mechanisms on top of each other as modules. In discrete actuation, the actuation amounts are a limited number of certain values. It is not feasible to solve the kinematic analysis problems of discretely actuated hyper-redundant manipulators (DAHMs) by using the common methods, which are used for continuous actuated manipulators. In this paper, a new method is proposed to solve the trajectory tracking problem in a static prescribed obstacle field. To date, this problem has not been considered in the literature. Theremoving first collision(RFC) method, which is originally proposed for solving the inverse kinematic problems in the obstacle fields was modified and used to solve the motion planning problem. For verification, the numerical results of the proposed method were compared with the results of thegenetic algorithm(GA) method. Furthermore, a novel DAHM designed and implemented by the authors is introduced.

Motion Planning for a Redundant Manipulator Using Genetic Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 782-787 ◽  
Author(s):  
S. Parasuraman ◽  
Chiew Mun Hou ◽  
V. Ganapathy
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Collision Detection ◽  
Angular Displacement ◽  
Joint Space ◽  
Redundant Manipulators ◽  
Evaluation Function ◽  
Redundant Manipulator ◽  
End Effector ◽  
Total Displacement

The trajectory planning of redundant manipulator is key areas of research, which require efficient optimization algorithms. This paper presents a new method that combines multiple objectives for trajectory planning and generation for redundant manipulators. The algorithm combines collision detection, finding target and optimizing trajectory using Genetic algorithm. In order to optimize the path, an evaluation function is defined based on multiple criteria, including the total displacement of the end-effector, the total angular displacement of all the joints, as well as the uniformity of Cartesian and joint space velocities. These criteria result in minimized, smooth end-effector motions. These algorithm yields solutions instantaneously and generate the path. The proposed algorithm is analyzed and its performance is demonstrated through simulation and the results are compared with the other methods.

scholarly journals Optimal Point-to-Point Trajectory Tracking of Redundant Manipulators using Generalized Pattern Search

10.5772/5781 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 24 ◽  
Author(s):  
Atef A. Ata ◽  
Thi Rein Myo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Trajectory ◽  
Pattern Search ◽  
Redundant Manipulators ◽  
Redundant Manipulator ◽  
Optimal Point ◽  
Intermediate Point ◽  
End Effector ◽  
Generalized Pattern Search ◽  
Point To Point

Optimal point-to-point trajectory planning for planar redundant manipulator is considered in this study. The main objective is to minimize the sum of the position error of the end-effector at each intermediate point along the trajectory so that the end-effector can track the prescribed trajectory accurately. An algorithm combining Genetic Algorithm and Pattern Search as a Generalized Pattern Search GPS is introduced to design the optimal trajectory. To verify the proposed algorithm, simulations for a 3-D-O-F planar manipulator with different end-effector trajectories have been carried out. A comparison between the Genetic Algorithm and the Generalized Pattern Search shows that The GPS gives excellent tracking performance.

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