Optimal Time-Jerk Trajectory Planning for Delta Parallel Robot Based on Improved Butterfly Optimization Algorithm

In this paper, a multi-objective integrated trajectory planning method based on an improved butterfly optimization algorithm (IBOA) is proposed, to improve the dynamic performance of the Delta parallel pickup robot in high-speed pick-and-place processes. The main objective of the present study is to improve dynamic positioning accuracy and running stability at high speeds and high accelerations. On the one hand, the intention is to ensure smooth motions using the trajectory planning method, and on the other hand to improve the picking efficiency. To this end, the pick-and-place trajectory of the robot is constructed by using NURBS curves in Cartesian space. Taking the time and jerk as the optimization objectives, a trajectory optimization method based on the improved butterfly optimization algorithm (IBOA) is proposed. The IBOA is based on the butterfly optimization algorithm (BOA); a circle chaotic sequence is introduced to replace the random initial population of the original BOA, and the fractional differential is used to improve the convergence speed of the BOA. Then, the problem of parallel segment deformation of the optimized trajectory is solved. Finally, a three-degrees-of-freedom Delta robot is used to evaluate the performance of the prosed algorithm. The obtained results show that, compared with other optimization algorithms, IBOA reduces the optimization time by 16.2%, and the maximum jerk is reduced by 87.6%. The results are better than the optimization results of other algorithms by 14.1% and 27.2%. The robot motion simulation results show that IBOA can effectively reduce the vibration acceleration of the end platform.

Automated summary

This paper proposes a multi-objective integrated trajectory planning method based on an improved butterfly optimization algorithm (IBOA), aiming to improve the dynamic performance of the Delta parallel pickup robot in high-speed pick-and-place processes. The method focuses on improving dynamic positioning accuracy and running stability at high speeds and accelerations.