*3.4. Contrastive Experiments in Real Environments*

To test the performance of TO-RRT in actual picking, the Franka manipulator was taken as the moving object, the citrus as the operation object, and the tree trunk as the obstacle avoidance object to construct a multi-objective citrus-picking environment. The environmental parameters are shown in Tables 7 and 8. First, the manipulator adjusted its pose to the initial state, and its joint angle was (0, <sup>−</sup>*<sup>π</sup>* <sup>4</sup> , 0, <sup>−</sup>*<sup>π</sup>* <sup>2</sup> , 0, *<sup>π</sup>* <sup>3</sup> , 0). Second, the threedimensional coordinates of the citrus, the parameter information of obstacles, and the picking pose of the manipulator were transmitted to the planning thread, and the continuous and collision-free trajectory was obtained through inverse kinematics. Finally, MoveIt! published the trajectory through moveit\_commander to move\_group and transmitted the control signal to the robot controllers to complete the picking action. The control block diagram is shown in Figure 15. The experimental results showed that the TO-RRT algorithm could be used to effectively reduce the nodes, shorten the planning time, and reduce the movement time of the manipulator, as shown in Figure 16 and Table 9.

**Table 7.** Obstacle information.


**Table 8.** Target information.


**Figure 15.** Control block diagram.

**Figure 16.** The manipulator reached Citrus 1 and Citrus 2 and avoided the branches. (**a**) Initial state of manipulator; (**b**) The manipulator reaches the first citrus; (**c**) Obstacle avoidance of the manipulator; (**d**) The manipulator reaches the second citrus.

**Table 9.** Comparison of the planning time and movement time.


#### **4. Discussion**

*4.1. Analysis*

From Figure 10a–d, since the RRT algorithm did not consider the effect of target offset probability, the entire workspace was searched in all environments. The above problems led to the huge scale of the random tree and caused more collision detection times. Therefore, the path length and movement time of the manipulator were the longest among all the algorithms, as shown in Tables 6 and 9. From Table 2, the biased-RRT algorithm avoided redundant searching through heuristic guidance, effectively reducing the number of tree nodes and collision detection times. From the average index in Table 2, since the RRT-BCR algorithm removed nodes that collided multiple times, its node failure growth rate was very low. However, this approach took a considerable amount of computation time, only 0.0112 s less than the biased-RRT algorithm, as shown in Table 9. From the average index in Table 2, the path length of the NC-RRT algorithm was the shortest, and the running time was second only to the TO-RRT algorithm. As can be seen from the multi-rectangle environment in Table 2, the NC-RRT algorithm had to continuously expand its sampling space when facing obstacles with large occlusion areas, resulting in 55,077 collision detections (which was the highest among all the algorithms). From Table 2, the TO-RRT algorithm reduced the numbers of path nodes and collision detections through an attractive step size, reduced the number of node failure growth through the node-first search strategy, and, finally, enhanced the escape ability through the regression superposition algorithm. However, the TO-RRT algorithm produced larger steps near obstacles, which led to a slightly longer path length than the other improved algorithms, as shown in Table 6.

#### *4.2. Future Work*

Industry 5.0 is a new generation of the industrial revolution representing "personalization", in which personalized products and services are created for humans by using the creativity of human experts to interact with efficient, intelligent, and precise machines. The key technologies of Industry 5.0, such as human–computer interaction, collaborative robots, and edge computing (EC), can provide ideas and technical support for Agriculture 5.0 [39].

As the number of China's aging population increases by the year, the number of rural employees has dropped sharply, and original agricultural production methods can no longer meet the development needs of the current citrus industry. Through the high integration of artificial intelligence and mechanical equipment, the transformation and upgrade of the production mode of China's agricultural industry can be realized. The improved method proposed in this paper can be used in the fields for picking robots and pruning robots and for the path planning of orchard patrol robots [40–42]. By analyzing the characteristics of a citrus tree environment, the work presented in this paper aimed to optimize the time required and improve it on the basis of a traditional algorithm to greatly shorten the planning time of the manipulator and reduce the movement time of the manipulator to a certain extent. However, the detection of obstacles is an objective challenge faced by this method.

In recent years, path planning through deep reinforcement learning (DRL) has become a research hotspot. A robot senses environmental information through sensors and trains the samples in the process of continuous interaction with the environment to complete an efficient, accurate, and low-environment-dependence path-planning method. The fusion of deep reinforcement learning and traditional path-planning algorithms has gradually become a research trend. For example, LM-RRT determines the selection probability of extension and connection trees based on reinforcement learning and guides the trees to pass through narrow channels quickly [43]. Based on this, the research on improving the TO-RRT algorithm by reinforcement learning will be discussed in the next stage.
