Research on Path Planning Optimization Based on the Integration of Model Predictive Control and A* Algorithm

To address the challenges of efficiency and adaptability in robotic path planning within dynamic environments, this study proposes an optimized method that integrates Model Predictive Control (MPC) with the A* algorithm. Based on a kinematic model of a differential drive robot, the proposed method combines the global optimal path search capability of the A* algorithm with the dynamic optimization features of MPC, achieving collaborative global and local optimization in complex environments. Experimental results demonstrate that the integrated algorithm significantly enhances path smoothness, obstacle avoidance capability, and real-time performance. Compared to single-algorithm approaches, the integrated algorithm reduces computational complexity and improves adaptability. Additionally, the study identifies challenges such as the computational burden associated with an extended MPC prediction horizon and the search efficiency of the A* algorithm in large-scale maps, suggesting directions for future optimization. This research offers a new technical pathway for autonomous robot navigation in dynamic environments, holding significant theoretical value and practical implications for the application of intelligent robotics technology.