This paper proposes a nonlinear model predictive control (MPC) framework initialized using an initial-guess particle swarm optimization (IG-PSO) algorithm for mobile robots navigating in environments with nonconvex obstacles. The proposed method is designed to address the local minimum problem inherent in conventional optimization-based MPC by incorporating a PSO-based global search method to generate effective initial guesses. In addition, a grid-based representation of the nonconvex obstacles is implemented to systematically define the collision avoidance constraints within the MPC formulation. The proposed method was validated in real-time simulations using the Robot Operating System (ROS) and the Gazebo physics simulator. The results demonstrate that the proposed MPC initialized by IG-PSO generates collision-free trajectories that avoid local minima and track the desired reference trajectory in environments with nonconvex obstacles. Compared with conventional IPOPT-based MPC, the proposed method exhibited improved performance in the tested scenario. The proposed method also maintains real-time control capabilities by selectively activating the IG-PSO algorithm only as required. The findings of this study demonstrate the potential of the proposed framework for robust and efficient trajectory planning in complex, nonconvex obstacle environments.
Seung‐Mok Lee (Wed,) studied this question.