Robot Time Optimal Trajectory Planning Based on Improved Simplified Particle Swarm Optimization Algorithm

Abstract

In order to tackle the robot trajectory planning problem with the short running time as the optimization goal, a time-optimal trajectory planning algorithm was presented based on improved simplified particle swarm optimization (ISPSO). The robot's trajectory was constructed by 3-5-3 polynomial interpolation in the joint space of the robot. Under the condition of satisfying the velocity constraint, the objective function was constructed by the sum of the time intervals between each node. ISPSO was used to optimize the objective function. The algorithm was improved by optimizing the inertia weight updating method and introducing a golden sine segmentation algorithm as an optimization operator. Compared with other particle swarm optimization algorithms, ISPSO had higher search velocity and accuracy. The effectiveness of the proposed algorithm was demonstrated through simulations using the PUMA 560 industrial robot, which resulted in a 19% reduction in time compared to the simplified particle swarm algorithm. The simulation results show that ISPSO achieved time optimization under the condition of velocity constraint, which proved its superiority in trajectory planning.

Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm

“…However, when only a cubic polynomial is used, the angular acceleration is discontinuous, and when only using a quintic polynomial to pass through multiple path points, it leads to complex calculations. Therefore, we used the 3-5-3 piecewise polynomial interpolation [23] to conduct trajectory planning, which reduces computational complexity, effectively ensuring the manipulator's stability while passing through multiple path points. The 3-5-3 piecewise polynomial interpolation is divided into three segments, with the first and third segments being cubic polynomial and the second segment being quintic polynomial.…”

“…Chaotic mapping has good ergodicity and randomness [36], providing a more uniform initial population distribution than conventional random number generators by generating chaotic sequences, and increasing the population diversity. Therefore, we used Circle chaotic mapping to initialize the population, and the generated chaotic sequence was as follows [37]: (23) where mod (a, b) is the remainder of a over b, and k (i) is the ith chaotic sequences number. The generation of a Circle chaotic sequence does not rely on initial values, and k (1) can be a random number between [0,1].…”

“…Piecewise polynomial interpolation has proven to be a feasible method for guaranteeing trajectory smoothness while simplifying calculation [21,22]. The 3-5-3 piecewise polynomial [23] combines the advantages of cubic and quintic polynomials, enabling the stable operation of the manipulator and without the need for complex calculations in trajectory planning. It is suitable for the trajectory planning of the woodworking manipulator.…”