Trajectory Optimization of High-Speed Robotic Positioning with Suppressed Motion Jerk via Improved Chicken Swarm Algorithm

For the trajectory optimization of the time-jerk of robotic arms with a chicken swarm optimization algorithm, using five-order B-spline interpolation can ensure smooth and continuous acceleration, but, due to the performance problems of the algorithm, the low solution accuracy and the slow convergence speed, the ideal trajectory curve cannot be obtained. To address these problems, an improved chicken swarm algorithm based on a parallel strategy and dynamic constraints (PDCSO) is proposed, where the rooster update method is employed with a parallel strategy using X-best guidance and a Levy flight step. Dynamic constraints for the rooster are given, followed by the hens, and the optimal rooster position that improved the convergence accuracy while preventing the local optimum was determined. Simulation experiments using 18 classical test functions showed that the PDCSO algorithm outperformed other comparative algorithms in terms of convergence speed, solution accuracy and solution stability. Simulation validation in ADAMS and real machine tests proved that PDCSO can effectively reduce the running time and motion shock for robotic arms and improve the execution efficiency of such arms.

Automated summary

Using a five-order B-spline interpolation ensures smooth and continuous acceleration for trajectory optimization of robotic arms. However, the algorithm suffers from performance issues, resulting in low solution accuracy and slow convergence speed. To overcome these issues, an improved chicken swarm algorithm called PDCSO is proposed, which incorporates parallel strategy and dynamic constraints. Simulation experiments and tests validate that PDCSO outperforms other algorithms in terms of convergence speed, solution accuracy, and stability, reducing running time and improving execution efficiency of robotic arms.