scholarly journals Residual Vibration Reduction Control of a Two-Link Flexible Robot Arm Using Optimal Trajectory Planning based on Genetic Algorithm.

2001 ◽  
Vol 19 (7) ◽  
pp. 905-912 ◽  
Author(s):  
Hiroyuki Kojima ◽  
Tetsuji Kibe
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Vibration Reduction ◽  
Robot Arm ◽  
Flexible Robot ◽  
Residual Vibration ◽  
Optimal Trajectory Planning

Evolutionary Learning Acquisition of Optimal Joint Angle Trajectories of Flexible Robot Arm

2006 ◽  
Vol 18 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Hiroyuki Kojima ◽  
◽  
Takahiro Hiruma
Keyword(s):  
Genetic Algorithm ◽  
Joint Angle ◽  
Fitness Function ◽  
Vibration Reduction ◽  
Evolutionary Learning ◽  
Robot Arm ◽  
Flexible Robot ◽  
Residual Vibration ◽  
Fifth Order ◽  
Acquisition Method

This paper proposes the evolutionary learning acquisition method of the optimal joint angle trajectories of a flexible robot arm using the genetic algorithm is proposed, and the effects of the optimal joint angle trajectories obtained by the present evolutionary learning acquisition method on the residual vibration reduction are ascertained numerically and experimentally. In the construction of the evolutionary learning acquisition algorithm of the optimal joint angle trajectories, the joint angular velocity curves are depicted with fifth-order polynomials, and, by considering the boundary and constraint conditions, they are expressed by four parameters. Then, the residual vibrations of the flexible robot arm are expressed as a function of the chromosome consisting of four parameters, namely, four genes, and a fitness function of the genetic algorithm for the residual vibration reduction is defined. Furthermore, the numerical calculations have been carried out, and it is confirmed that the residual vibrations almost disappear. Moreover, the experimental results are demonstrated, and the usefulness of the present evolutionary learning acquisition method of the optimal joint angle trajectories of the flexible robot arm using the genetic algorithm is ascertained experimentally.

Energy Optimal Trajectory Planning of a Robotic Manipulator Using Genetic Algorithm

2010 ◽  
Author(s):  
Aritra Biswas ◽  
B. L. Deekshatulu ◽  
Shibendu Shekhar Roy ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Robotic Manipulator ◽  
Optimal Trajectory Planning

scholarly journals Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

2021 ◽  
Author(s):  
Yiping Meng ◽  
Yiming Sun ◽  
Wen-shao Chang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Particle Swarm ◽  
Adaptive Genetic Algorithm ◽  
Swarm Optimization ◽  
Timber Joint ◽  
Timber Joints ◽  
Optimal Trajectory Planning

AbstractIn this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

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