Singularity-free trajectory planning of platform-type parallel manipulators for minimum actuating effort and reactions

Robotica ◽  
2008 ◽  
Vol 26 (3) ◽  
pp. 371-384 ◽  
Author(s):  
Chun-Ta Chen ◽  
Hua-Wei Chi
Keyword(s):  
Trajectory Planning ◽  
Particle Swarm Optimization Algorithm ◽  
Numerical Technique ◽  
Parallel Manipulators ◽  
Singular Points ◽  
Planning Problem ◽  
Control Points ◽  
Swarm Optimization ◽  
Trajectory Representation

SUMMARYDue to the existence of singular configurations within the workspace for a platform- type parallel manipulator (PPM), the actuating force demands increase drastically as the PPM approaches or crosses singular points. Therefore, in this report, a numerical technique is presented to plan a singularity-free trajectory of the PPM for minimum actuating effort and reactions. By using the parametric trajectory representation, the singularity-free trajectory planning problem can be cast to the determination of undetermined control points, after which a particle swarm optimization algorithm is employed to find the optimal control points. This algorithm ensures that the obtained trajectories can avoid singular points within the workspace and that the PPM has the minimum actuating effort and reactions. Simulations and discussions are presented to demonstrate the effectiveness of the algorithm.

A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

2019 ◽  
Vol 233 (16) ◽  
pp. 6019-6031
Author(s):  
Jing Zhang ◽  
Wu Yu ◽  
Xiangju Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Model Simulation ◽  
Particle Swarm ◽  
Planning Model ◽  
Swarm Optimization ◽  
Multi Phase ◽  
Multi Mode

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.

B-Spline Curve Fitting Based on Adaptive Particle Swarm Optimization Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1299-1304 ◽  
Author(s):  
Yue Hong Sun ◽  
Zhao Ling Tao ◽  
Jian Xiang Wei ◽  
De Shen Xia
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Control Points ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curve

For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.

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