## 2 Materials and Methods
### Design and Test of Harvesting System
#### 2.4.2 Simulation of Planning Algorithm  
A path-planning algorithm was used to generate a collision-free path for the picking manipulator. Compared with those of apple, strawberry, kiwi, and other crops, more attention must be paid to picking efficiency in the tea-picking process. As the picking robot's path planning will directly affect the efficiency of picking operations, it is one of its key technologies. As a search algorithm based on random adoption, Rapidly-exploring Random Tree (RRT)--first proposed by [PERSON] [16] in 1998--is the most popular manipulator path-planning algorithm at present, which generates nodes via random sampling. Then, the tree node closest to the new node is selected through step size expansion, and if there is a collision, it is discarded; otherwise, the nodes are joined. This process is repeated until the target node is reached. RRT is widely used in assisting with picking robot's path planning, due to its high efficiency and fast planning performance. However, the traditional RRT algorithm has blindness and efficiency problems, which has become the focus of academic research [17]. In order to optimize the algorithm, researchers have focused on improving search efficiency and path quality while maintaining its completeness and flexibility. For example, based on the traditional RRT algorithm, the RRT-connect algorithm has been proposed by introducing the strategy of bidirectional search and greedy linking [18]; a novel RRT-star algorithm has been proposed to optimize the connections between the explored nodes [19], which takes into account the dynamic characteristics and constraints of the manipulator; and a heuristic T-RRT algorithm has been proposed to ensure that the robot arm can be used for path planning in complex environments [20].  
Therefore, in this study, the RRT, RRT-connect, RRT-star, and T-RRT algorithms were simulated 50 times. The industrial control computer described in Section 2.3.5 was used as the experimental equipment, and Ubuntu 22.04 was selected as the operating system. Furthermore, the simulation environment was built based on Robot Operating System 2 (ROS2). During the experiment, using the MoveIt! motion planning framework, the configuration package initiated the model documents of the manipulator and set the trajectory of the manipulator. Specifically, the starting origin (0, 0, 0), the initial position of the manipulator (0.50, 0.29, 0.07), and the target position (\(-\)0.20, 0.47, 0.56) were defined. The motion path of the manipulator was strictly defined to attain the target position through the initial position from the initial origin, and the maximum number of searches was set to 1024. Therefore, if a path was found within the maximum number of searches, the path search was considered successful; otherwise, the path search was considered to be a failure. The objective deviation of the test parameters was set to 0.05, and the step length was set to 0.4 degrees. The 3D visualization tool Rviz was used to observe the motion-planning effect of the manipulator during the test, and relevant data were generated in the planning process for statistical analysis, as shown in Figure 10.