Trajectory Planning Algorithm for Parallel Platforms Integrating DDPG and Efficient Forward Kinematics

Parallel platforms are widely used in simulators and precision machining due to their high precision and high rigidity. Trajectory planning is crucial to achieve high-performance motion control for platforms, while existing algorithms suffer from complex kinematics, difficult multi-objective optimization and low efficiency. Therefore, this thesis combines Efficient Forward Kinematics (EFK) with Deep Deterministic Policy Gradients (DDPG) to form the EFKD trajectory planning algorithm. Simulation and experimental results show the convergence speed and success rate of the EFKD algorithm are improved by at least 40% compared with the Cartesian Space Linear (CSL) algorithm. The speed and acceleration indicators are continuously smooth, which effectively suppresses the problem of sudden acceleration change.