In response to the dense and randomly positioned growth of citrus branches, leaves, and fruits, to achieve safe interactive picking of interlaced and overlapping branches and fruits, a citrus obstacle avoidance picking method was proposed. To enhance the positioning accuracy and picking efficiency, the hand-eye calibration problem was transformed into solving the equation T1X = XT2 , completing the calibration from the camera coordinate system to the base coordinate system of the robotic arm. For the citrus open-center tree shape in the natural environment, tree skeleton extraction based on point cloud density was conducted, and noise reduction processing of the branch and trunk point clouds was performed through the point density threshold method to increase the operation speed. The octree map method was utilized to construct the obstacle map of branches and trunks, and the hierarchical bounding box method was employed to fit the robotic arm and carry out collision detection. With the objective of time optimization, an improved RRT connect obstacle avoidance planning algorithm that conforms to the agricultural requirements of picking was proposed. Target bias was introduced to the RRT connect algorithm for optimizing and guiding the sampling points. To verify the feasibility of this obstacle avoidance method, taking the citrus orchard with standard dwarf and dense planting cultivation as the research object, an obstacle avoidance system for the picking robot was established. Multiple sets of obstacle avoidance picking experiments were respectively conducted for citrus fruits growing inside the fruit tree and close to the trunk in the natural environment. The experimental results indicated that the obstacle avoidance movement time for fruits growing close to the trunk was 9.5 s, and the success rate of obstacle avoidance picking was 91% ;for fruits growing inside the fruit tree, the obstacle avoidance movement time was 10.5 s, and the success rate of obstacle avoidance picking was 88% .