# RJA-Star Algorithm for UAV Path Planning Based on Improved RSDOS Model
## 1 Introduction  
Smart agriculture means that rural workers use advanced technology and experience to carry out agricultural production. The development of social, economic and technological smart agriculture in rural areas is closely related [1]. which is essential in eliminating poverty, helping developed economies \"catch up\" and forming strategies that promote development in China. Intelligent agriculture includes areas such as: internet technologies, wireless sensor technology [2; 3] and remote control technology. As a very important part of intelligent equipment, UAVs are the focus of more attention because of their practical usefulness. UAVs are widely used in agriculture [4; 5], forestry [6], disaster relief [7], and geological exploration [8]. The Agricultural UAV [9; 10] plays an important role in the fields of crop monitoring, crop yield assessment and plant protection. However, when the UAV sprays precisely at low altitude, farmland obstacles such as plant protection network, residence, electric pole, communication tower, lighting objects and various organisms will pose a serious threat to the UAVs [11]. The main task in agricultural work is to effectively avoid obstacles and achieve the set goals. Significant research has been carried out to solve this problem. Autonomous flight can be achieved to a certain extent by means of sensors, reliable control algorithms and pre-measured obstacle position information [12; 13]. A large amount of path planning and obstacle avoidance algorithms have been developed for the path planning of UAVs. For example, A-star algorithm [14], ant colony algorithm [15], artificial potential field [16], DIJKSTRA [17] and so on. Deep learning technology can also be used as an effective method for UAV path planning. A-star algorithm is an effective tool, but the computational complexity of the traditional A-star algorithmspace will increase exponentially with the size of the map, resulting in a significant increase in computational complexity and computational time. In order to solve these problems, domestic and foreign scholars have done significant research. [PERSON] et al. [18]., proposed a zigzag global planner based on a-star, which improves the search efficiency of a-star algorithm in narrow space. [PERSON] et al. [19]., proposed an improved A-star algorithm based on terrain data, reducing the calculation time of the algorithm. [PERSON] et al. [20]., added A-star algorithm to the artificial potential field algorithm, which optimizes the path xu of the UAV and better reflects the actual environment of the UAV. [PERSON] et al. [21]., improved the genetic algorithm with A-star algorithm. [PERSON] et al. [22]., adopted the idea of collision ji to improve A-star algorithm. [PERSON] et al. [23]., proposed an improved A-star algorithm combined with the jump point search algorithm to reduce the computational cost of A-star algorithm. However, there are still problems in using the nearest neighbor interpolation method to process the path. To sum up, the a-star algorithm in two-dimensional space has been greatly improved. But there is little improvement regarding the three-dimensional space A-star algorithm [24]. [PERSON] et al. [25], made a detailed comparison on the path planning strategies of star a, Lazy Theta and Theta in a three-dimensional environment. [PERSON] et al. [26], constructed an improved path planning model using three-dimensional two-way sector expansion method and variable step search strategy. Although the moving distance is reduced by 7.53%, the time cost is increased by 2.66 times. These researches still have the problems of complex computation and large time cost, to solve this problem, this paper presents an algorithm of RJA-star (R5 DOS Jump A-star) based on the R5 DOS(RCC5-Direction-Octant-Strongly-exists model) model in three-dimensional space. Firstly, the topology of UAV and UAV detection area is expressed, and the A-star algorithm is improved by the JPS(jump point search) algorithm.