## 5 Simulation and Experiment of Manipulator Path Planning
### Experiment and Simulation Research on the Application of Manipulator Path Planning  
A manipulator experimental platform was established as shown in Figure 12 to verify the feasibility of the proposed method in the practical manipulator. The hardware of the experimental platform included the 6-DOF manipulator [PERSON]'s Robot Elfin E05, the supporting tools and the computer were equipped with the Ubuntu20 operating system. ROS Noetic MoveIt 1 is an open-source robotic motion planning framework for robot motion planning and control. It provides collision checking and control functionalities, integrating collision detection libraries such as the Flexible Collision Library (FCL), which is used to detect collisions between the robot and the environment or other objects. Manipulator simulations were conducted to test the path planning. The use of the simulation environment allowed us to perform multiple experiments under different scenarios, conditions, and parameter settings to ensure the rationality and feasibility of the planned paths, reducing the wear and tear on the manipulator in real-world experiments. Figure 12: The experimental platform for the manipulator. The simulation environment shown in Figure 12 was established for the obstacle avoidance path planning task of the manipulator using Rviz and Movelt1. The simulation environment consisted of four different obstacle environments and various starting positions and target positions, all of which closely resembled real-world scenarios. In the CDTD3 algorithm, \(\lambda\)\(=\) 0.1, \(c\)\(=\) 1, and \(i\)\(=\) 100. A total of 100 simulation experiments were conducted on the manipulator. The average length of the executed path in the joint space and the success rate of planning execution results are presented in Table 6, the threshold \(\varphi\) in the experiment was 5 (cm). According to the results in Table 6, the paths generated by CDTD3-RRT* had a good performance on different tasks, with shorter paths after planning and execution. In Scenario 3, where the task was relatively simple, the results of CDTD3 and TD3 were similar. Moreover, under four different tasks, three algorithms had a higher success rate in path planning. In the four scenarios, the paths obtained through the CDTD3-RRT* had a better length compared to those obtained through RRT*. The path reduction rates were 11.1%, 31%, 9.0%, and 20.4%, respectively. The manipulator experimental platform is shown in Figure 12. The process of obtaining the executable path of the manipulator is shown in Figure 13. When conducting path planning experiments on manipulator, given the starting pose, target pose and obstacle information. CDTD3-RRT* is first used to compute a collision-free path based on the environment. Then, the inverse kinematics solution and obstacle avoidance detection were performed using the motion planning method computed cartesian path in MoveIt1, which integrates IKFast and FCL plugin. Thus, the executable path of the joint space was calculated, and information such as joint variables required for the operation of the manipulator was output. After obtaining the executable path information, it was sent to Elfin E05 for execution. CDTD3-RRT* was tested multiple times to verify the performance in different targets. During the operation of the manipulator, a ROS node was utilized to collect real-time joint parameters, which were recorded in the file for further analysis. The comparison between the manipulator's end path and the planned trajectory is illustrated in Figure 14. To accurately analyze the error, the trajectory was decomposed into three axes: the \(x\)-axis, \(y\)-axis, and \(z\)-axis. Although there were errors in each axis direction of the manipulator's end path, the error was in a small range, which was consistent with the deviation between the actual experiment and the planned path.