Time-Optimal Trajectory Planning of Industrial Robots Based on Particle Swarm Optimization

2015 ◽  
Author(s):  
Mingyu Gao ◽  
Pan Ding ◽  
Yuxiang Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Particle Swarm ◽  
Industrial Robots ◽  
Swarm Optimization ◽  
Time Optimal ◽  
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.

scholarly journals Solving the Time-Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Shaotian Lu ◽  
Jingdong Zhao ◽  
Li Jiang ◽  
Hong Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Particle Swarm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Best Value ◽  
Augmented Lagrange Multiplier ◽  
Optimal Trajectory Planning

The problem of minimum time-jerk trajectory planning for a robot is discussed in this paper. The optimal objective function is composed of two segments along the trajectory, which are the proportional to the total execution time and the proportional to the integral of the squared jerk (which denotes the derivative of the acceleration). The augmented Lagrange constrained particle swarm optimization (ALCPSO) algorithm, which combines the constrained particle swarm optimization (CPSO) with the augmented Lagrange multiplier (ALM) method, is proposed to optimize the objective function. In this algorithm, falling into a local best value can be avoided because a new particle swarm is generated per initial procedure, and the best value gained from the former generation is saved and delivered to the next generation during the iterative search procedure to enable the best value to be found more easily and more quickly. Finally, the proposed algorithm is tested on a planar 3-degree-of-freedom (DOF) robot; the simulation results show that the algorithm is effective, offering a solution to the time-jerk optimal trajectory planning problem of a robot under nonlinear constraints.

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