# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 1 Introduction
In contrast, the grid method is more appropriate because it can determine the complete obstacle shape by setting smaller grid sizes. A path-planning algorithm is necessary for a robot to realize the task of global glass wall cleaning, which demands the planned path to be as short as possible with a low repetition rate. To solve the problem posed by the global coverage of operational areas, a series of path-planning algorithms have been proposed. The cattle tillage covering algorithm (BCD) proposed by [PERSON] et al. divides a working area into several subareas, and then the robot uses reciprocating or spiral motion to cover the area in each subarea [32]. When encountering obstacles, the robot uses a back-off operation to complete the covering task. [PERSON] et al. proposed a full-coverage path-planning algorithm for a sweeping robot [33], which used a priority inspiration function to determine the moving position of the robot and used a genetic algorithm to solve the problem of falling into dead zones. [PERSON] et al. presented a path-planning robot based on a rolling window priority heuristic algorithm for a wall-climbing robot, using a rolling window combined with a mobile traversal priority rule implementation area, preventing the robots from being trapped in dead zones [34]. [PERSON] et al. designed a new fusion algorithm based on the improved A* and Open_Planner algorithms (A-OP), improving the smoothness of the path using the minimum snap method and A-OP algorithm to implement the path planning of an unmanned sweeper. [35]. [PERSON] et al. proposed a cognitive space-time environment modeling approach for autonomous vehicles, which models a multiscenario-adapted space-time environment model from a cognitive perspective and transforms the scenario-based trajectory planning problem into a unified spatial-temporal planning problem [36]. [PERSON] and [PERSON] developed a method to generate and sort homotopy classes according to a lower bound heuristic estimator. The classes were used to constrain and guide path-planning algorithms [37]. [PERSON] and [PERSON] presented a review of the most successful coverage path-planning (CPP) methods, which determine a path that passes over all points of an area or volume of interest while avoiding obstacles. Furthermore, they discussed the reported field applications of the described CPP methods [38]. [PERSON] et al. investigated CPP for the robotic single-sided dimensional inspection of free-form surfaces. Others improved CPP by replacing the random viewpoint sampling strategy [39]. [PERSON] et al. proposed a CPP method based on deep reinforcement learning for a kiwifruit-picking robot, using LiDAR to collect the environmental point cloud information of the kiwifruit orchard and construct a two-dimensional grid map; an improved deep reinforcement learning algorithm, the re-DQN algorithm, was proposed to determine the traversal order of each region [40]. In summary, existing rope-driven robots face the following problems in obstacle overcoming and path planning. 1. The robot can complete cleaning operations on simple wall surfaces, but moving from one piece of glass to another is difficult, as these panes of glass are separated by large barriers such as balconies or partitions. 2. The existing path-planning algorithms generally ignore the inset area of U-shaped obstacles, which limits the cleaning coverage rate. 3. The dead zone escape strategy has not been widely applied in facade cleaning path planning, so the robot can become stuck in dead zones and unable to choose a direction. To address the above problems, we used five winches to establish a spatial model to drive the robot body through ropes, so that it has three degrees of freedom of movement to overcome obstacles. Using the grid method to build the wall model to mark obstacles, we then decomposed it according to the wall curvature to more thoroughly clean subareas.