Accelerating Deep Neural Network training for autonomous landing guidance via homotopy

Abstract

The feasibility of utilizing Deep Neural Networks for generating guidance commands in spacecraft landing scenarios has garnered recent research interest. A primary challenge lies in the inefficiency of training sample generation. This paper proposes a new approach to address it by utilizing nonlinear homotopy methods, which involve embedding the homotopic parameter either into the performance index or the right-hand side of the differential equations. Unlike the existing approaches that overlooked the potential value of the intermediate solutions during homotopy process, the proposed method incorporates these discarded solutions as supplementary training data, which treats the homotopic parameter and state as inputs, and the control as the output. Two examples, namely a minimum-fuel lunar landing and a minimum-time asteroid landing, are provided to illustrate the effectiveness and advantages of the proposed method.