SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

• URL: http://arxiv.org/abs/2405.09212v1
• Date: Wed, 15 May 2024 09:38:52 GMT
• Title: SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics
• Authors: Yifan Liu, You Wang, Guang Li,
• Abstract summary: Model Predictive Control (MP)-based trajectory planning has been widely used in, and Control Barrier (CBF) can improve its constraints. In this paper, we propose a self-supervised learning algorithm for CBF-MPC trajectory planning.
• Score: 13.129654942805846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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