  Model Predictive Control (MPC)-based trajectory planning has been widely used
in robotics, and incorporating Control Barrier Function (CBF) constraints into
MPC can greatly improve its obstacle avoidance efficiency. Unfortunately,
traditional optimizers are resource-consuming and slow to solve such non-convex
constrained optimization problems (COPs) while learning-based methods struggle
to satisfy the non-convex constraints. In this paper, we propose SOMTP
algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory
planning. Specifically, first, SOMTP employs problem transcription to satisfy
most of the constraints. Then the differentiable SLPG correction is proposed to
move the solution closer to the safe set and is then converted as the guide
policy in the following training process. After that, inspired by the Augmented
Lagrangian Method (ALM), our training algorithm integrated with guide policy
constraints is proposed to enable the optimizer network to converge to a
feasible solution. Finally, experiments show that the proposed algorithm has
better feasibility than other learning-based methods and can provide solutions
much faster than traditional optimizers with similar optimality.
