Path Planning Algorithm for Unmanned Ground Vehicles in Unstructured Off-Road Environments

As autonomous driving technologies continue to evolve, their real-world applications are expanding beyond structured urban environments. However, off-road autonomous driving remains a challenging problem due to the unstructured and unpredictable nature of such terrains. For an unmanned ground vehicle (UGV) to drive reliably in off-road environments, a path planning algorithm must not only generate smooth trajectories that avoid abrupt changes but also ensure drivability by adapting to irregular terrain features. In this paper, we propose a novel and efficient path planning algorithm tailored for off-road driving. Our method, called two-step MPC-PSO, combines Model Predictive Control (MPC) with Particle Swarm Optimization (PSO) to generate optimal paths within a limited computational budget. We also design a cost function that explicitly accounts for off-road conditions to enhance terrain adaptability. We validate our approach through experiments conducted at two off-road test sites. The results demonstrate that our method generates paths with low maximum curvature and sufficient path length, enabling smooth and continuous driving in unstructured environments.