التفاصيل البيبلوغرافية
| العنوان: |
基于相邻争夺算法的无人机多架次植保作业路径规划. (Chinese) |
| Alternate Title: |
Path planning for the multiple drones plant protection operations of UAV based on adjacent contention algorithm. (English) |
| المؤلفون: |
沈跃, 张凌飞, 沈亚运, 储金城, 刘慧 |
| المصدر: |
Transactions of the Chinese Society of Agricultural Engineering; Aug2024, Vol. 40 Issue 16, p44-51, 8p |
| مصطلحات موضوعية: |
PLANT protection, PARTICLE swarm optimization, FLIGHT testing, ENERGY consumption, BUILDING protection, VIRTUAL reality |
| Abstract (English): |
Traditional particle swarm optimization(PSO) is prone to getting stuck in local optima and low search ability for energy consumption optimal solutions when planning plant protection operation paths. The study focuses on improving the PSO algorithm in order to improve the efficiency of crop protection drone operations and reduce drone losses. The study proposes an adjacent competition (AC) algorithm for crop protection operation path planning based on a crop protection UAV model in order to solve the problem of poor effectiveness of traditional particle swarm optimization algorithms applied to UAV plant protection operations. The adjacent competition algorithm ensures that the total distance of crop protection UAV operations is constant, provides prior knowledge of the search direction, and ensures that special points are not missed, preventing the algorithm from getting stuck in local optimal solutions: Firstly, all particles initialize the homework distance range to prevent extreme situations where the single homework distance is too long or too short. Secondly, each drone is randomly assigned the operating distance for each particle within the operating range. Finally, adjacent particles compete with each other for the operating distance, indirectly changing the operating distance of each flight and searching for the optimal path. The study used Matlab to conduct algorithm simulation verification on a simulated plant protection site of 420 m×200 m. Traditional particle swarm optimization algorithm often gets stuck in local optima and the final planning results fluctuate greatly, while the adjacent competition algorithm obtains the optimal energy consumption solution 9 times out of 10 planning iterations, and the planning energy consumption of non-optimal solutions is similar to the optimal energy consumption, still better than all planning results of the traditional particle swarm algorithm, proving that the neighboring contention algorithm has stronger searchability. The results show that traditional particle swarm optimization algorithms often got stuck in local optima and increased energy consumption by 16.16% to 38.14% compared to neighboring contention algorithms in 10 rounds of path planning. This indicated that the energy consumption of the adjacent competition algorithm proposed in this study for planning results is much lower than that of the traditional particle swarm optimization algorithm, and the algorithm has stronger search capabilities. This study used the RflySim simulation platform to build a crop protection drone model and a 420 m×200 m work site in order to compare the simulated tracking results of the traditional particle swarm optimization algorithm and adjacent competition algorithm in a virtual environment. The results show that the energy consumption of adjacent competition algorithm planning results is reduced by 25.15% compared to the traditional particle swarm algorithm. This study conducted actual flight tests using the drone M300 at a 420 m×200 m work site to validate the practicality of the algorithm. Actual flight experiments have shown that the energy consumption of adjacent contention algorithm planning results is reduced by 34.48% compared to traditional particle swarm optimization algorithms, making it more suitable for multiple crop protection operations. In actual experiments, the return points of multiple UAVs in the adjacent contention algorithm are closer to the supply point, the distance of departure and return is shorter, and the ineffective energy consumption is lower compared with the traditional particle swarm optimization algorithm, indicating that its planning is more reasonable. Adjacent particles indirectly change the distance of each operation by competing for each other's operation distance, effectively improving the convergence speed of the algorithm, preventing the algorithm from getting stuck in local optima, and improving the efficiency of multiple crop protection operations with a single drone. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
为了提高植保无人机作业效率, 减少无人机作业损耗, 该研究针对传统粒子群 (particle swarm optimization, PSO)算法在规划植保作业路径时容易陷入局部最优, 能耗最优方案的搜索能力低下等问题, 该研究提出一种基于相邻争夺(adjacent competition, AC)算法的植保无人机作业路径规划算法。首先, 对所有粒子设置作业距离范围, 以防止单次作业距离过长或过短的极端情况；其次, 在作业距离范围内随机分配每个粒子的作业距离, 作为搜索的初始值；最后, 相邻粒子相互争夺作业距离间接改变各架次作业距离, 搜索出最优路径。相邻争夺算法保证了植保无人机作业总距离一定, 对搜索方向进行先验且保证特殊点不被遗漏, 避免算法陷入局部最优解。使用Matlab对420 m×200 m的模拟植保场地进行算法仿真验证, 传统粒子群算法常陷入局部最优解, 在10次规划中相较于遍历出的能耗最优规划方案增加了16.16%～38.14%的能耗, 本文提出的相邻争夺算法规划结果的能耗远低于传统粒子群算法, 算法具有更强的搜索能力。使用RflySim仿真平台搭建植保无人机模型和420 m×200 m的作业场地, 在虚拟环境下比较传统粒子群算法与相邻争夺算法规划结果的模拟跟踪情况, 相邻争夺算法规划结果的能耗相较传统粒子群算法减少了25.15%。420 m×200 m作业场地实际飞行试验中, 相邻争夺算法规划结果的能耗相较传统粒子群算法减少了34.48%, 更适应多架次植保作业。 [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
