Robot Time-Optimal Trajectory Planning Based on Improved Cuckoo Search Algorithm - Wang, Wenjie; Tao, Qing; Cao, Yuting; Wang, Xiaohua; Zhang, Xu (IEEE access, 2020)
The trajectory planning time is uncertain when there is no velocity constraint in the joint space of an operating robot. This paper proposes a 3-5-3 polynomial interpolation trajectory planning algorithm based on improved cuckoo search algorithm (ICS) which functions under a velocity constraint. Kinematics analysis of the robot is conducted and the 3-5-3 polynomial interpolation function is established as the foundation for trajectory planning. The size of the step size control factor is set as a variant which varies with the number of iterations, which accelerates the algorithm's convergence and prevents premature falling into the local optimal solution, both problems in the traditional cuckoo search algorithm where the step size control factor is fixed. The proposed algorithm was used to optimize the trajectory of a test robot with interpolation time as the search space for optimization. Any interpolation time that did not meet the speed constraint was eliminated to secure the shortest possible robot running time. The UR robot was taken as a research object to simulate the joint motion trajectory planning in MATLAB. The proposed algorithm performed well and better realized the time-optimal trajectory under the constraint of speed compared to the traditional cuckoo search algorithm, particle swarm optimization algorithm, and genetic algorithm.