# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 5 Experiment and Results  
To verify the feasibility of the algorithm, we built a simulation experimental platform. The outdoor glass curtain wall was replaced with an office glass partition, and the obstacles were simulated using raised cubes randomly placed on the partition. The cleaning robot was a robot prototype with a control system, as shown in Figure 13. The robot prototype contained a cleaning body and five winches equipped with encoders for feedback rope length data. The winch size was 10 \(\times\) 10 \(\times\) 30 cm, the rope length was 9 m, the body size was 25 \(\times\) 25 \(\times\) 10 cm, and the body weight was 3.62 kg. Winches 1 and 2 were mounted on the wall 2.5 m above the ground, with a distance of 3 m between them. Winches 3 and 4 and 1 and 2 formed a rectangle. Winch 5 was mounted on the ground 3 m from the wall. For the convenience of analysis, a working coordinate system with winch 3 as the origin was established, where the \(x\) axis was horizontally forward from winch 3 to 4, the \(y\) axis was opposite to the direction of gravity, and the \(z\) axis followed the right-hand spiral rule. In addition, a control system was used to adjust the winding/releasing of the ropes for each winch and obtain real-time rope length data. An observation camera (MV-CE060-10 UM) fixed 4 m in front of the wall was used to record robot movement, and the actual position of the robot on the wall could be obtained via combining the size-proportional relationship between the image and experimental platform.  
Figure 12: Full-coverage path planning: (**a**) wall model; (**b**) U obstacles; (**c**) dead zone; (**d**) coverage area.  
Figure 13: Experimental platform.  
To verify the effectiveness of the improved priority heuristic full-coverage path-planning algorithm, the environment shown in Figure 13 was used for the experiment, wherein some different-shaped obstacles were distributed on the glass wall with an existing dead zone.  
Before the experiment, the grid was modeled on the operating environment, the positions of the obstacles were marked, and the cleaning body was set to start from the lower-left corner of the map. The full-coverage algorithm proposed in this paper was implemented. The results show the escape route of the body when it encountered a dead zone. As shown in Figure 14, the cleaning robot applied the improved priority heuristic full-coverage path-planning algorithm for the area coverage task. In the process of covering the area, the cleaning body could avoid obstacles. When the body became trapped in the dead zone, the dead zone escape strategy was executed, and the robot successfully escaped from the dead zone.  
To further verify the accuracy of the full-coverage path-planning algorithm, we set 20 points in this scene to calculate the theoretical robot position at these points, and then we measured the actual position. Finally, the position errors were obtained by comparing the two values. The experimental data are shown in Figure 15.  
Figure 14: Full-coverage path-planning experiment.  
The experimental results show that the proposed improved priority heuristic full-coverage path-planning algorithm could be applied to accurately complete the whole-area coverage task and is an efficient full-coverage path-planning algorithm.