Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hi-index | 0.00 |
Due 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.