In order to solve the problems of large amounts of calculation and long calculation times of the A-star algorithm in three-dimensional space, based on the R5DOS model, this paper proposes a three-dimensional space UAV path planning model. The improved R5DOS intersection model is combined with the improved A-star algorithm. Together, they construct a local search process, and the R5DOS path planning model is established by reducing the number of search nodes. The path planning model is simulated through MATLAB software and the model can greatly reduce the number of nodes and computational complexity of the A-star algorithm in three-dimensional spaces, while also reducing the calculation time of the UAV. Finally, we compare the improved A-star algorithm with the original A-star algorithm and the geometric A-star algorithm. The final fitting result proves that the improved A-star algorithm has a shorter computation time and fewer node visits. Overall, the simulation results confirm the effectiveness of the improved A-star algorithm and they can be used as a reference for future research on path planning algorithms.

To improve the obstacle avoidance ability of agricultural unmanned aerial vehicles (UAV) in farmland settings, a three-dimensional space path planning model based on the R5DOS model is proposed in this paper. The direction layer of the R5DOS intersection model is improved, and the RJA-star algorithm is constructed with the improved jump point search A-star algorithm in our paper. The R5DOS model is simulated in MATLAB. The simulation results show that this model can reduce the computational complexity, computation time, the number of corners and the maximum angles of the A-star algorithm. Compared with the traditional algorithm, the model can avoid obstacles effectively and reduce the reaction times of the UAV. The final fitting results show that compared with A-star algorithm, the RJA-star algorithm reduced the total distance by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%. Compared with the geometric A-star algorithm, the running time of the RJA-star algorithm is reduced by 95.84%, the number of nodes is reduced by 99.95%, and the number of turns is reduced by 67.28%. In general, the experimental results confirm the effectiveness and feasibility of RJA star algorithm in three-dimensional space obstacle avoidance.

When investigating precision pest management strategies for fruit farmlands with complex geometries and restrictive boundaries, this study proposes an enhanced coverage optimization methodology for agricultural drones based on an enhanced Black-winged Kite Algorithm (BKA). Initially, the task area is segmented using the Segment Anything Model (SAM) based on deep learning, and an environmental map is created through gridding. Subsequently, by proposing coverage task cost functions, flight safety cost functions, and path length cost functions, the coverage challenge in complex-shaped areas is redefined as a challenge involving multiple constraints. To optimize this problem, we introduce a DWBKA that incorporates a Dynamic Position Balancing strategy and a modified Whale Random Walk strategy, thereby enhancing its global search capability and avoiding local optima traps. Finally, comparative experiments are conducted in six distinct scenarios of fruit farms, juxtaposing the DWBKA with the initially developed version and the BL-DQN. The results of this comparative analysis unequivocally demonstrate that the DWBKA achieves superior performance metrics, excelling in coverage rate, repeated coverage rate, path length, and computational time. When compared with extant coverage methodologies for complex shapes, the proposed DWBKA method exhibits marked performance enhancements in coverage tasks. This underscores its potential to significantly elevate the efficiency and precision of drone coverage in complex farm settings.