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.

Traditional manual or semi-mechanized pesticide spraying methods often suffer from issues such as redundant coverage and cumbersome operational steps, which fail to meet current pest and disease control requirements. Therefore, there is an urgent need to develop an efficient pest control technology system. This paper builds upon the Deep Q-Network algorithm by integrating the Bi-directional Long Short-Term Memory structure to propose the BL-DQN algorithm. Based on this, a path planning framework for pest and disease control using agricultural drones is designed. This framework comprises four modules: remote sensing image acquisition via the Google Earth platform, task area segmentation using a deep learning U-Net model, rasterized environmental map creation, and coverage path planning. The goal is to enhance the efficiency and safety of pesticide application by drones in complex agricultural environments. Through simulation experiments, the BL-DQN algorithm achieved a 41.68% improvement in coverage compared with the traditional DQN algorithm. The repeat coverage rate for BL-DQN was 5.56%, which is lower than the 9.78% achieved by the DQN algorithm and the 31.29% of the Depth-First Search (DFS) algorithm. Additionally, the number of steps required by BL-DQN was only 80.1% of that of the DFS algorithm. In terms of target point guidance, the BL-DQN algorithm also outperformed both DQN and DFS, demonstrating superior performance.

Traditional pesticide application methods pose systemic threats to sustainable agriculture due to inefficient spraying practices and ecological contamination. Although agricultural drones demonstrate potential to address these challenges, they face critical limitations in energy-constrained complete coverage path planning for field operations. This study proposes a novel BiLG-D3QN algorithm by integrating deep reinforcement learning with Bi-LSTM and Bi-GRU architectures, specifically designed to optimize segmented coverage path planning under payload-dependent energy consumption constraints. The methodology encompasses four components: payload-energy consumption modeling, soybean cultivation area identification using Google Earth Engine-derived spatial distribution data, raster map construction, and enhanced segmented coverage path planning implementation. Through simulation experiments, the BiLG-D3QN algorithm demonstrated superior coverage efficiency, outperforming DDQN by 13.45%, D3QN by 12.27%, Dueling DQN by 14.62%, A-Star by 15.59%, and PPO by 22.15%. Additionally, the algorithm achieved an average redundancy rate of only 2.45%, which is significantly lower than that of DDQN (18.89%), D3QN (17.59%), Dueling DQN (17.59%), A-Star (21.54%), and PPO (25.12%). These results highlight the notable advantages of the BiLG-D3QN algorithm in addressing the challenges of pesticide spraying tasks in agricultural UAV applications.

Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture.

Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.

The Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSSP) represents a core challenge in modern manufacturing: achieving synergistic optimization of multiple conflicting objectives while pursuing production efficiency and energy sustainability. To address this, this study proposes an enhanced hybrid heuristic algorithm—KNN–Tabu Search NSGA-II (KTNSGA-II)—for simultaneously optimizing completion time, machine load, and total energy consumption. First, a three-objective mathematical model is established. Subsequently, four key strategies are integrated: (1) workload balancing initialization rapidly generates high-quality initial solutions; (2) an adaptive job-level crossover mechanism dynamically adjusts subset sizes during iterations to balance global exploration and local exploitation; (3) K-nearest neighbor-based congestion distance calculation maintains population diversity; (4) tabu search applied to non-dominated solutions on the Pareto front for local refinement. Extensive experiments on standard benchmark instances demonstrate that KTNSGA-II significantly outperforms representative algorithms in terms of convergence and diversity. For large-scale Behnke benchmark instances, KTNSGA-II achieves an average hypervolume (HV) improvement of 32.32% compared to other comparison algorithms. Furthermore, this method substantially enhances solution diversity: the Spacing Performance (SP) metric improved by 39.72%, indicating more uniform distribution of Pareto optimal solutions; the Diversity Metric (DM) increased by 57.54%, reflecting broader coverage and more even distribution along the Pareto frontier boundary. These results confirm that KTNSGA-II generates higher-quality, better-distributed Pareto fronts, achieving a more optimal trade-off between completion time, machine load, and energy consumption.