Time-Optimal Trajectory Planning for Robot Arm Using Hybrid Strategy Particle Swarm Optimization

To enhance the motion efficiency and operational stability of robot arms in industrial production under the kinematic constraints of robot arms, this paper proposes a 3-5-3 polynomial interpolation time-optimal trajectory planning method based on a hybrid strategy particle swarm optimization algorithm. Firstly, the 3-5-3 polynomial function is introduced as the research basis for trajectory planning, and the kinematic constraints are used for its solution. Then, a hybrid strategy particle swarm optimization algorithm is proposed based on the advantages and disadvantages of the traditional particle swarm optimization algorithm. In the population initialization, the tent chaotic mapping is introduced, and the dynamic learning factor and adaptive inertia weight are introduced in the algorithm iteration process to optimize the local and global search capabilities of the algorithm and avoid the problem that the algorithm is prone to falling into local extremums. Finally, the time optimization objective function is established, and the improved particle swarm optimization algorithm is used to minimize the running time of the robot arm trajectory. The simulation test is carried out using the MATLAB software. The test results show that compared with the initial set time, the time consumption is reduced by 29.5%, and compared with the traditional particle swarm optimization algorithm, the time consumption is reduced by 9.5%. While effectively improving the convergence speed of the algorithm, the running time of the robot arm is shortened, verifying the effectiveness and feasibility of the algorithm.