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In order to solve the problems of low efficiency and poor picking success rate in the path planning of picking robotic arm in unstructured environment, a four-way search RRTstar algorithm combined with artificial potential field method was proposed. Firstly, the space was segmented by the artificial potential field method to obtain the spatial segmentation point x-split for four-way search;secondly, the random sampling was guided by the artificial potential field method to improve the quality of the sampled nodes;then, based on the node history expansion information, the information factor was added to achieve the adaptive dynamic step size expansion. Finally, the generation path was optimized by greedy backtracking. The effectiveness of the proposed algorithm was verified by two-dimensional simulation experiments, simulation experiments under 6-degree-of-freedom robotic arm and picking experiments. The 2D simulation comparison experiment showed that compared with the RRTstar algorithm, the path cost of the improved algorithm was shortened by 2.01%, the time cost was reduced by 98.81%, and the sampling nodes were reduced by 92.49%. Simulation experiments under 6-degree-of-freedom robotic arm in robot operating system (ROS) showed that compared with RRTstar algorithm, the planning time was reduced by 93.17%, the path cost was reduced by 36.62%, and the number of expansion nodes was reduced by 97.00%. Finally, the picking experiment was carried out with a 6-degree-of-freedom robotic arm, and the experimental results showed that the algorithm effectively improved the picking success rate, which reached 85%, and after combining the attitude constraint method, the picking success rate reached 95%. The proposed path planning method had certain advantages in path planning time, and the picking test proved that the algorithm improved the success rate of lychee picking, and contributed to the development of lychee picking robot.