GMPPO: Guided Model Predictive Path Optimization for Mobile Robots in Obstacle-Constrained Environments

Autonomous navigation of industrial mobile robots in obstacle-constrained environment, such as manufacturing floors, warehouse storage systems, and factory inspection scenarios, poses significant challenges in ensuring path feasibility, safety, and real-time performance. This article proposes a guided model predictive path optimization (GMPPO) framework to address these issues. First, an enhanced convex feasible set algorithm is employed to generate robust initial paths by integrating decoupled dimensionality reduction and a dual-layer fault-tolerant mechanism. Second, based on this reference path, robot geometry, and real-time obstacle information, dynamic safety corridors are constructed to characterize local feasible spaces. Finally, these reference paths and corresponding safety corridors guide a high-fidelity model predictive optimization process, yielding smooth, dynamically feasible, and collision-free trajectories under nonholonomic constraints. Theoretical analysis establishes the resolution completeness and feasibility guarantee of the proposed method. Extensive simulations, real-world robotic experiments, and engineering deployments demonstrate that GMPPO significantly improves planning success rates, trajectory smoothness, and operational safety, while maintaining high computational efficiency and broad applicability.