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Differential dynamic programming (DDP) is a widely used and powerfultrajectory optimization technique, however, due to its internal structure, itis not exempt from local minima. In this paper, we present Differential DynamicProgramming with Escape Network (DDPEN) - a novel approach to avoid DDP localminima by utilising an additional term used in the optimization criteriapointing towards the direction where robot should move in order to escape localminima. In order to produce the aforementioned directions, we propose toutilize a deep model that takes as an input the map of the environment in theform of a costmap together with the desired goal position. The Model producespossible future directions that will lead to the goal, avoiding local minimawhich is possible to run in real time conditions. The model is trained on asynthetic dataset and overall the system is evaluated at the Gazebo simulator.In this work we show that our proposed method allows avoiding local minima oftrajectory optimization algorithm and successfully execute a trajectory 278 mlong with various convex and nonconvex obstacles.

DDPEN: Trajectory Optimisation With Sub Goal Generation Model