Optimized Model Predictive Control-Based Path Planning for Multiple Wheeled Mobile Robots in Uncertain Environments

Addressing the path planning problem for multiple wheeled mobile robots (WMRs) in uncertain environments, this paper proposes a multi-WMR path planning algorithm based on the fusion of artificial potential field and model predictive control. Firstly, an artificial potential field model for uncertain environments is established based on the APF method. Secondly, an MPC optimal controller that considers the artificial potential field model is designed to ensure the smooth avoidance of moving and concave obstacles by multiple WMRs in uncertain environments. Additionally, a formation control algorithm based on an enhanced APF method and the leader-follower algorithm is proposed to achieve formation maintenance, intra-formation collision avoidance, and obstacle circumvention, thereby ensuring formation stability. Finally, two sets of simulation experiments in uncertain environments demonstrate the effectiveness and superiority of the proposed method compared to the APF-MPC algorithm, enabling the control of multiple WMRs to reach their target positions safely, smoothly, and efficiently. Furthermore, two sets of real-world experiments validate the feasibility of the algorithm proposed in this paper.