Model Predictive Control of Autonomous Vehicles With Integrated Barriers Using Occupancy Grid Maps

Abstract

Nonlinear model predictive control (NMPC) is an efficient and proven method for optimization-based autonomous vehicle motion planning. Among the various approaches, the iterative linear quadratic regulator, a differential dynamic programming variant, is a well-known efficient nonlinear optimization method. In safety-critical control systems, controllers should address inequality-constrained optimization problems. In this letter, we design a single unified constraint using an occupancy grid map. We convert this inequality-constrained optimization problem into an unconstrained optimization problem by appending a single integrated discrete barrier state to the system model. This approach simplifies complex motion planning problems and reduces computational costs. In the proposed method, we first discretize the surrounding environment with an occupancy grid map and design a single constraint that ensures that only cells with values less than a predefined threshold can be traversed by the ego vehicle. Then, we define a single integrated discrete barrier state to introduce this constraint into the motion planning algorithm. The proposed method, a penalty method, and the augmented Lagrangian method are tested on a real-time software-in-the-loop simulation using CarMaker and ROS. The simulation results of pop-up obstacle avoidance scenarios show the benefits of the proposed method, such as reduced time costs and increased robustness.