# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 1 Introduction  
With the development of urban transportation, the emergence of driverless sweepers has greatly changed the way cities are cleaned. Replacing the traditional manual road-cleaning model with driverless [1; 2] sweeping not only improves cleaning efficiency, but also reduces labor costs. An important prerequisite for driverless sweepers to drive autonomously is to plan an optimal and safely executable path. The principle of path planning is to plan a path from the starting point to the end point based on regional environmental information and vehicle information, and it also needs to have an obstacle-avoidance function during the driving process. Path-planning algorithms can be divided into two categories, one is global path planning and the other is local path planning. Global path planning is a static planning algorithm, which plans an optimal route from the start point to the end point on the map based on the existing map environment information. Global-planning algorithms include classical algorithms such as the A* algorithm [3; 4], Dijkstra algorithm [5; 6], RRT algorithm [7; 8], and also intelligent algorithms such as the ant colony algorithm [9; 10] and genetic algorithm [11; 12]. Local path planning is implemented using a dynamic-planning algorithm [13; 14], which is based on a sensor sensing the surrounding environment to plan a path for the vehicle to drive safely, and is often applied to scenarios such as overtaking and obstacle avoidance [15]. Local-path-planning algorithms include the dynamic window method, artificial potential field method [16; 17], Open_Planner algorithm [18], Bessel curve algorithm [19; 20] and so on. There are also newly proposed neural network algorithms [21; 22], and others. The dominant planning algorithm used by many unmanned-vehicle planning frameworks is the A* algorithm, such as the move_base framework [23], and the Autoware framework [24]. For example, [PERSON]'s breadth search algorithm can complete the pathfinding operation, but it has the disadvantage of consuming a large amount of computer memory, being computationally intensive with many nodes, and having a high space and time complexity. It is, therefore, less often used for the actual path planning of unmanned vehicles. However, traditional A* algorithms suffer from the problem of low algorithm efficiency due to many redundant points, on the one hand, and low path smoothing in the case of many turning points, on the other hand, resulting in less smooth vehicle turns and extra time or power consumption when turning. At the same time, the A* algorithm cannot avoid unknown static or dynamic obstacles, so how to make the path smooth, improve planning efficiency and avoid obstacles is a problem that needs to be solved. In reality, the use of global path-planning algorithms alone does not guarantee the safety of an unmanned vehicle, as the presence of unknown obstacles can make the vehicle unsafe. Many researchers have conducted in-depth research and discussions on planning algorithms. [PERSON] [25] proposed a Maklink-based approach to address the shortcomings of the A* algorithm in terms of its computationally intensity and limitations of domain search strategies. [PERSON] [26] designed a method which introduced extended distance, bidirectional searching and smoothness into path planning to improve paths by reducing the number of turns. [PERSON] [27] proposed a conventional algorithm which does not consider the vehicle model and lacks speed planning. The safe-space configuration of the vehicle model was considered and a vehicle acceleration system was constructed to improve the applicability of the A* algorithm in vehicle planning. [PERSON] [28] combined a time-factor normalization model with a valuation function to reduce the cost of trip planning. [PERSON] [29] proposed a two-model predictive control framework which incorporates information from path planning into a dynamic windowing method for obstacle avoidance, ensuring that target locations are detected in an unknown environment.