A Space Discretization Method for Smooth Trajectory Planning of a 5PUS-RPUR Parallel Robot

To improve the dynamic performance of parallel robots in multi-dimensional space, a novel trajectory planning method of space discretization for parallel robots is proposed. First, the kinematic model of the 5PUS-RPUR parallel robot is established. Then, the normalized Jacobian condition number is obtained via the variable weighting matrix method, and is used as the performance metric of path optimization. The weighted sum method is utilized to construct a composite objective function for the trajectory that incorporates travel time and acceleration fluctuations. Next, the position space between the start and end points is discretized, and the robot pose space based on the position points is analyzed via the search method. The discrete pose point weights are assigned according to the condition number. Dijkstra’s algorithm is used to find the path with the minimum condition number. The trajectory optimization model is established by fitting the discrete path with a B-spline curve and optimized via genetic algorithm. Finally, comparative numerical simulations validate the proposed method, which reduces actuator RMS displacement difference by up to 32.9% and acceleration fluctuation by up to 25.6% against state-of-the-art techniques, yielding superior motion smoothness and dynamic stability.