# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 5 Simulation and Experiment
### Test in Simulated Field  
In order to verify the reliability of the system function and the planning algorithm, a large number of simulation experiments were carried out in the training field, as shown in Figure 22. The purpose is to verify the reliability of the robot's path planning in real scenarios. The operation process is recorded through video and database, which are used to evaluate the planning effect. For the path planning results, the evaluation indicators include: reliability (no collision), stability (whether the planning is completed), and optimality of the results (whether there are redundant trajectories). A total of 16 rounds of tests were carried out, and the results are shown in Table 3.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithm** & **RRT*** & **RRT-Connect** & **RRT***-Offline** \\ \hline Sample times & 1823 & 648 & 214 \\ \hline Collision detection times & 20,860 & 1003 & 1235 \\ \hline Calculation time (ms) & 6326 & 578 & 395 \\ \hline Path length (\({}^{\circ}\)) & 323.4 & 430.7 & 194.5 \\ \hline Path segment number & 12.1 & 15.5 & 9.3 \\ \hline Success rate & 85\% (4000) & 100\% & 100\% \\ \hline Path availability & 65\% & 45\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of of different algorithms for motion planning of 6-DOF robots.  
Figure 22: Simulated experiment scene.  
The results show that all kinds of algorithms can effectively avoid obstacles when the safe distance is set reasonably (safe distance \(>\) 5 cm). A potentially dangerous path means that the robot is too close to a charged object, such as the cross arm, during the movement. Experiments show that RRT*-offline can effectively avoid this risk through dynamic sampling.  
The stability of the algorithm is evaluated by whether the first plan is successful. All planning algorithms for wire stripping have a high success rate, but during the clamp installation operation, the success rate of RRT-connect and RRT* drops sharply due to the existence of the cable. The main problem is that the cable's constraints on the robot's movement cannot be solved. RRT*-offline avoids this problem through dynamic area sampling.  
The optimality of the result refers to the ratio of the path length of the first planning result to the shortest path length of the 10 times planning under the simulation conditions. The smaller the value, the closer the planning result is to the optimal solution. Due to the limitation of the number of iterations (5000), the planning effect of RRT* algorithm is inferior to RRT*-offline, and the performance of RRT-connect algorithm is the worst.  
The experiment shows that the path planning constraints designed according to the task characteristics have achieved the expected results. As shown in Figure 23a-c, posture constraints are added to the robot path planning, the angle between the direction of gravity and the closing direction of the fixed parts is always greater than 90 degrees, so as to prevent it from closing under the action of gravity. As shown in Figure 23d-f, the longer tool size results in a larger load torque on the robot. Through the torque constraint, the robot is in a better output attitude and the load capacity is improved. As shown in Figure 23g-i, under the effect of preference constraints, the robot body is far away from the end to avoid cable winding.