# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 1 Introduction  
With the development of autonomous driving technology and the structural adjustment, transformation and upgrading of the sanitation industry, the market demand for intelligent equipment is expanding, primarily including the demand for automatic driving sweeping equipment. The realization of unmanned sweepers' unique use and driving characteristics has important practical significance and broad market prospects [1]. The market volume of unmanned sweepers in China and the U.S. in recent years is shown in Figure 1, where 2020-2022 is the data from research and 2023-2025 is the forecast data. As shown in Figure 2, for urban roads, garbage is usually distributed at the edge of the road, so it is more appropriate to use a fixed path for cleaning strategy, but for closed parks such as schools, parks, airports, etc., the garbage is more dispersed and the efficiency of the traditional directional cleaning is difficult to guarantee. The full-coverage path can be used in parks, but too long a path will consume a lot of energy, and the efficiency is low; it will also interfere with the normal passage of pedestrians and vehicles in the park. Existing robots lack a global view, so sweeping can be blind. An effective approach is to enhance the target perception within the current visible sweeping, and then design a corresponding sweeping strategy to improve the environmental adaptability of the sweeper. However, this process faces two challenges: on the one hand, the existing perception strategy can hardly meet the demand of forming a qualitative and dense perception of the road garbage distribution when the sweeper plans its route. The existing machine vision perception of existing sweepers or sweeping robots is based on target classification, and although some road targets can be identified through machine vision, and even obstacles and garbage can be distinguished [2], the spatial continuity of information obtained by target identification is poor [3], and it is difficult to form accurate continuous perception for densely distributed garbage. Although semantic segmentation can be enhanced in terms of spatio-temporal continuity, it can only recognize regions with a single feature and has poor performance in recognizing regions composed of complex features. On the other hand, existing intelligent body planning algorithms for self-driving vehicles and robots use a heuristic search as the computational core. Such methods often have the shortest path as the optimization goal to form optimal path planning. However, for unmanned sweepers, their planning core is optimally sweeping-oriented, forming the shortest path possible under the guarantee of sweeping efficiency. The change in optimization goals driven by the sweeping task makes the original path planning based on the heuristic search form an inefficient sweeping route. In addition, the randomness and dispersion of the garbage distribution in the cleaning make the target in the cleaning path planning process no longer a collection that can be spatially characterized in traditional planning methods, but a class of spatially discrete sets of each anisotropy. More complex planning objectives make the traditional planning algorithm no longer applicable, and there is an urgent need to study an environment-adaptive dynamic planning algorithm for unmanned sweepers. Figure 1: The market volume of unmanned sweepers in China and the U.S. in recent years and in the future (source: Statistical Bulletin on the Development of the Transport Sector). Figure 2: Different garbage distribution patterns for urban roads and enclosed parks.