Grid-Based Integrated Safety-Critical Motion Planning for Unmanned Mobile Vehicles

This is the authors' abstract. We don't add key points for this paper.

Nonlinear model predictive control is a reliable and effective approach for optimization-based motion planning. In safety-critical control systems, controllers must solve inequality-constrained optimization problems. However, scene understanding, which consists of perceiving the driving environment and designing safety constraints, may complicate the optimization problem and is a resource-intensive process. In this paper, we propose a unified scene understanding method that uses occupancy grid maps (OGMs) to design a single unified constraint. We also propose a novel method for designing OGMs that method accounts for noise and uncertainties. We use this OGM approach for scene understanding to design a single constraint that ensures that only cells with occupancy probability values less than a predefined threshold can be traversed. We embed this constraint into the optimization problem as a single unified discrete barrier state. In the experiments, we compare the performance of the proposed method with that of an augmented Lagrangian method. The motion planning results in a pop-up obstacle avoidance scenario using an unmanned mobile vehicle demonstrate the advantages of the proposed method, such as reduced time costs and improved safety.