In order to address the issues of low trajectory planning efficiency and easy to fall into local solutions of unmanned agricultural vehicles in complex and narrow unstructured environments, an improved bidirectional A* algorithm combined with optimal control method is proposed in this paper. Firstly, the direction-guided search is introduced and the heuristic function is improved to accelerate the speed of bidirectional A* path planning in large-scale complex environments. Additionally, a path smoothing strategy is designed to reduce the number of inflection points and improve the quality of the reference path. Next, to address the challenge that the difficulty of handling obstacle avoidance constraints in optimal control problems increases significantly with the density of obstacles, safe driving corridors are constructed to reduce the impact of environmental complexity on computational efficiency. Finally, a penalty iteration framework is established based on the vehicle’s nonlinear kinematic model to solve optimization problems iteratively, thereby improving the success rate of trajectory planning and obtaining globally optimal or approximately optimal trajectories. In three different scale map simulations, the results show that compared with A* algorithm, the proposed improved bidirectional A* algorithm reduces the planning time and path length by 48.0% and 5.2%, and the path is smoother. In the unmanned agricultural vehicle trajectory planning, compared with Hybrid A* algorithm, variant 1 and variant 2, the proposed method reduces the cost of generated trajectory by 19.3%, 5.4% and 33.1%, respectively. The trajectory quality has obvious advantages, and provides an effective solution for practical application.