| 基于农田环境的农业机器人群协同作业策略 |
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| 引用本文： | 宫金良,王伟,张彦斐,兰玉彬.基于农田环境的农业机器人群协同作业策略[J].农业工程学报,2021,37(2):11-19. |
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| 作者姓名： | 宫金良 王伟 张彦斐 兰玉彬 |
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| 作者单位： | 1.山东理工大学机械工程学院,淄博 255049;2.山东理工大学农业工程与食品科学学院,淄博 255049 |
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| 基金项目： | 山东省引进顶尖人才"一事一议"专项经费资助项目;山东省重点研发计划项目（2019GNC106127）;淄博市生态无人农场研究院项目（2019ZBXC200） |
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| 摘 要： | 为合理分配农业机器人群协同作业中各机器人的工作量与工作区域,提高机器人群协同作业的整体效能与工作效率,该研究提出一种复杂环境下异质农业机器人群的任务分配及全区域覆盖策略。在考虑农业机器人异质性的基础上,以机器人团队整体效能最优为目标进行任务分配并确定各机器人的工作量。根据农场实际工作环境建立一级分区的概念,在栅格化环境建模与障碍物膨胀处理的基础上,在一级分区内部建立二级分区的栅格分区和分区合并规则,简化农田中的复杂工作环境;将遗传算法与混合粒子群算法相结合改进遗传算法交叉操作,建立遗传算法染色体种群多样性的概念,并综合考虑遗传算法染色体适应度值的差异以及种群多样性阶段设置自适应交叉变异概率,继而利用改进的遗传算法解决深度优先搜索算法在一级分区与二级分区间的遍历顺序问题;设置深度优先搜索算法在二级分区内的路径搜索规则,并在栅格图内遍历的同时根据各机器人的工作量分配其工作区域,设置机器人在其工作区域中的遍历规则,实现机器人群对农田的全区域覆盖。仿真试验结果表明,改进的遗传算法所得到的遍历各分区的路径长度与收敛迭代次数较传统遗传算法分别减少了2.8%与69.5%,较模拟退火算法分别减少了9.3%与19.0%;包含3、5、7、9和11个障碍物的5幅环境地图中,机器人群遍历工作区域的总面积重复率分别为6.3%、8.9%、16.7%、21.7%和23.4%。在4种面积相等的异形农田中设置相同数量的障碍物进行验证试验,结果表明,机器人群总遍历面积重复率分别为16.7%、13.1%、11.9%和6.7%。机器人群协同作业场地试验结果表明,4个试验机器人均可在规定的时间要求(25 min)内完成各自工作量,遍历面积重复率分别为5.77%、4.14%、6.75%和4.85%。研究结果可为复杂环境下农业机器人群协同作业策略提供理论支撑。
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| 关 键 词： | 机器人 路径规划 遗传算法 深度优先搜索算法 协同作业 |
| 收稿时间： | 2020/6/25 0:00:00 |
| 修稿时间： | 2020/10/27 0:00:00 |
Cooperative working strategy for agricultural robot groups based on farmland environment |
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| Gong Jinliang,Wang Wei,Zhang Yanfei,Lan Yubin.Cooperative working strategy for agricultural robot groups based on farmland environment[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(2):11-19. |
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| Authors: | Gong Jinliang Wang Wei Zhang Yanfei Lan Yubin |
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| Institution: | 1.School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China;;2.School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255049, China |
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| Abstract: | Abstract: This study aims to propose a collaborative control strategy with the task assignment and whole area coverage for heterogeneous groups of agricultural robots under the complex environment of a farmland. The new system was also utilized to reasonably allocate the workload and work area of each robot in the collaborative operation of agricultural robot groups, and thereby to improve the overall work efficiency in precision agriculture. Various performance parameters were considered, including the energy consumption, failure rate, historical workload, and service quality of agricultural robots. Taking the overall efficiency of a robot team as the optimization goal, the agricultural robots were selected to effectively perform the specific task, where the workload of each participating robot was determined in the collaboration control framework. The complex working environment of agricultural robots was set, according to the actual characteristics in the digital ecological circular farm of the Shandong University of Technology and Zibo Hefeng Seed Company, China. A field experiment was carried out on the whole area coverage strategy of robot groups. The farmland that separated by the intertwining road was taken as a primary partitioning, according to the actual farm working environment. The primary partitions were rasterized for environmental modeling operations, and thereby the common working range of robot groups was set to the unit length of cells in the grid. Specifically, the irregular obstacle was treated by the expansion operation in binary morphology of image processing, when the edge line of obstacle cannot align to the edge line of raster in the simulation. In the modeling of rasterized environment and the treatment of obstacle expansion, the raster partitioning at the second level was established inside the first level partitioning. The merging operation between the raster partitions was also conducted to reduce the number of partitionings in the raster diagram. The operations expansion and reduction were carried out for the enlarged obstacles after the partition merging. The Genetic Algorithm(GA) and Hhybrid Particle Swarm Optimization (HPSO) were combined to improve the traditional GA crossover operation in computer-assisted support systems. The genetic diversity and chromosome population structure were utilized to enhance the convergence speed, where the chromosome was crossed with the chromosome of real-time optimal fitness value in iterative process. The GA concept of chromosome population diversity was established to consider the differences of fitness value for the chromosome in GA, and the species diversity in the different phases, thereby to set the adaptive crossover probability and mutation probability. The improved GA was then used to solve the problem of traversal sequence in a depth first search algorithm, when traversing over the partitions of the first and the second level. The path search rules were set in the second level partition, where the starting point and end point of a traversal were determined in the depth-first search algorithm. The work area of robot was allotted according to the workload of robot using the depth first search algorithm, when traversing in the raster chart. The traversal rules of robot in the working area were set by A* algorithm and eight-neighborhood search algorithm, further to realize the complete coverage of robot groups on the whole region. The simulation results showed that the path length of traversing each partition, iteration times, and the converge time to the optimal solution in the improved GA were 2.8%, 69.5%, and 64.2% less than those of the traditional GA, while, 9.3%, 19.0%, and 9.9% less than those of the simulated annealing algorithm. When the total area of obstacles remained unchanged, the total area repetition rate of robot population covering the whole area increased, as the number of obstacles increased. The total area repetition rate of obstacle with the highest dispersion in the map was 23.4% in the simulation experiment. Four types of special farmland were set, where there were different shapes but the same total amount of workload. 7 obstacles with the same positions, shapes, sizes and quantities were set in each special shaped farmland. The total area repetition rates of robot were 16.7%, 13.1%, 11.9%, and 6.7%, when traversing the four farmlands. The experimental results showed that the task assignment and regional coverage scheme can achieve the robot group at work full coverage of work area within the prescribed time. The work strategy of robot groups can provide a theoretical support for collaborative operation of agricultural robot groups on the complex environment in modern agriculture. |
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| Keywords: | robots path planing genetic algorithm depth first search algorithm |
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