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Aiming at the issues such as prolonged path planning time, low efficiency and poor success rate of the picking manipulator in the apple picking task as a consequence of the complex natural picking environment, an improved fusion and switching path dynamic planning algorithm was proposed. The algorithm introduced a dynamic threshold goal bias sampling strategy and artificial potential field to alter the generation position of new nodes, increasing the purposiveness of sampling and improving convergence speed. A relative distance was incorporated into the repulsive potential field coefficient to overcome the problem of unreachable targets by considering the distance to the goal. To enhance the algorithm’s robustness, a threshold was set to partition the spatial region, dynamically switching to the failure-guided adaptive sampling region RRT algorithm (FGA-RRT) based on the current node expansion state to address narrow passage issues and increase planning success rates. The greedy algorithm was utilized to optimize the resulting path tree, removing redundant nodes, further shortening the path length, and optimizing path smoothness to ensure the stable movement of the picking robot arm. Simulation experiments were conducted for the RRT algorithm, RRT* algorithm, GB-RRT algorithm, common fusion algorithm and the improved fusion and switching algorithm respectively in simple obstacles, narrow channels, complex obstacles and simple three-dimensional spaces. The results showed that the improved fusion and switching algorithm had good adaptability in different environments, with high planning efficiency, few iterations and high path quality. Based on the established 6-DOF robot arm motion planning simulation environment and laboratory environment, obstacle avoidance picking tests were conducted. The improved hybrid switching algorithm’s picking efficiency was increased by 74.74%, path length was decreased by 32.03%, and picking success rate was improved by 8 percentage points compared with that of the RRT algorithm. The experimental results demonstrated that the proposed algorithm had stronger search capabilities in apple-picking scenarios, providing a reference for improving the operational efficiency of picking robot arms.