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.
Na et al. (2026) studied this question.