Large scale plant protection machinery in non-standard orchards in mountainous areas has poor accessibility, and small wheeled plant protection robots have broad application prospects. A path planning algorithm for wheeled plant protection robots based on improved ACO-DWA algorithm is proposed to solve the problems of visual information misjudgment caused by closed orchard branches and leaves, as well as delayed obstacle avoidance caused by complex working terrain. Firstly, the orchard environment information is obtained through LiDAR, and the voxel grid method is applied to simplify the point cloud density. The grid method is used to segment the ground point cloud, and the K-means algorithm is used to extract the robot’s inter row passage area. Combined with the kinematic model and job specification constraints of the plant protection robot, a series of candidate trajectory sets are generated using the model based prediction algorithm (SBMPO). Then, using the improved ACO-DWA algorithm, the robot’s travel cost is integrated into the objective function of the search node, and path planning is carried out online based on the environmental map. Finally, simulation validation and real-world deployment experiments were conducted using MATLAB R2021 simulation platform and robot ROS operating system, respectively. The experimental results show that this method can significantly improve the traffic capacity of robots in complex orchard scenes, and the path planning effect and operational efficiency are significantly improved.