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To continuously generate trajectories for serial manipulators with highdimensional degrees of freedom (DOF) in the dynamic environment, a real-timeoptimal trajectory generation method based on machine learning aiming at highdimensional inputs is presented in this paper. First, a learning optimization(LO) framework is established, and implementations with different sub-methodsare discussed. Additionally, multiple criteria are defined to evaluate theperformance of LO models. Furthermore, aiming at high dimensional inputs, adatabase generation method based on input space dimension-reducing mapping isproposed. At last, this method is validated on motion planning for hapticfeedback manipulators (HFM) in virtual reality systems. Results show that theinput space dimension-reducing method can significantly elevate the efficiencyand quality of database generation and consequently improve the performance ofthe LO. Moreover, using this LO method, real-time trajectory generation withhigh dimensional inputs can be achieved, which lays a foundation for continuoustrajectory planning for high-DOF-robots in complex environments.

Continuous Trajectory Planning Based on Learning Optimization in High Dimensional Input Space for Serial Manipulators