Time-optimal trajectory planning for robotic arms based on enhanced particle swarm optimization algorithm

In response to the issues of prolonged execution time and susceptibility to local optima in traditional Particle Swarm Optimization (PSO) algorithms for robotic arm trajectory planning, this paper proposes a 3-5-3 polynomial interpolation trajectory planning method based on an improved PSO algorithm. The algorithm dynamically optimizes the inertia weight and learning factor in the PSO algorithm and uses the sum of interpolation point time as the fitness function. Under velocity constraints, it achieves optimal time trajectory planning. The Elite EC66 robotic arm is selected as the research object, and simulations and data analysis are conducted using Matlab. Experimental results indicate that the improved PSO algorithm exhibits significantly enhanced convergence speed and optimization accuracy compared to traditional PSO algorithms, effectively reducing the time required for the robotic arm to complete target tasks.