A Data-Driven Real-Time Trajectory Planning and Control Methodology for UGVs Using LSTMRDNN

Dear Editor, This letter presents a novel data-driven trajectory planning and control scheme for the unmanned ground vehicles (UGVs). A recent work [1] has demonstrated the effectiveness of approximating the optimal state feedback for a nonlinear unmanned system via deep neural network (DNN). To further the previous research, we construct a long-short term memory recurrent deep neural network (LSTMRDNN) to improve the performance of the data-driven approximation instrument. The proposed strategy is evaluated and verified through theoretical analyses and experiments.