同种农机机群动态作业任务分配方法

为解决农机作业过程中因突发状况引起的作业时间增加、某些作业任务无法完成等问题,该研究提出一种基于改进合同网算法的同种农机机群动态作业任务分配方法。基于农机性能参数和任务参数综合考虑用时最长农机的作业时间、农机机群油耗和路上的路程建立机群代价函数,建立单个田块和地头相邻田块的路径规划方法,参考合同网算法中的招-投标过程,建立农机对任务进行投标的代价函数,基于降低服务器计算量、减少通信次数、任务均衡分配和减小非作业路程等原则,通过选择招标者、设定招标阈值、中标者任务再分配和农机间任务交换等方式改进合同网算法。在不同时间进行基于传统合同网算法和改进合同网算法的农机机群动态作业任务分配仿真试验和农场实际播种作业试验。仿真结果表明,基于改进合同网算法的动态任务分配仿真结果比基于传统合同网算法的动态任务分配仿真结果机群时间代价降低0.83%～12.89%,与服务器通信次数降低77.4%～85%。农场实际试验结果表明:在不同的任务分配时间,基于改进合同网算法的动态任务分配的机群时间代价比实际理论机群时间代价降低30.20%～34.09%。本文提出的动态作业任务分配法能够满足农业生产中同种农机机群动态作业任务分配需求。

Dynamic task allocation method for the same type agricultural machinery group

A multi-robot system often needs to change the robot''s behavior in response to dynamic environments, particularly in the field of multiple tasks in sustainable agriculture. Dynamic task allocation is therefore an essential requirement to improve the overall system performance for the same group type of multiple agricultural machineries. However, some challenges remained on the agricultural machinery group to efficiently determine the task assignments under local observations in some unexpected conditions. In this study, a dynamic task allocation strategy was proposed for the same type of agricultural machinery group using an improved Contract Net Protocol (CNP). A cost function was established for the task assignment and performance using the maximum operating time in the longest machinery, the fuel consumption, and the distance on the road of the agricultural machinery group. A path planning was developed to combine the straight and the bypass in the field operation for the single and adjacent fields using the highest efficiency of agricultural machinery. A task bidding was constructed for the cost function of agricultural machinery referring to the CNP bidding process. Some specific approaches were utilized in the improved CNP to balance tasks with fewer server calculations, communication time, and non-operational distances, ranging from the selection of tenderee, the setting of the bidding threshold and the task redistribution for successful bidder to the task exchange between agricultural machinery. A systematic simulation of dynamic task allocation was carried out for the newly added tasks and the failure of agricultural machinery, where the operating time was taken as the operating cost, while the agricultural machinery with different performances was taken as the same group. A field experiment was implemented on the multi-machine cooperative dynamic task allocation at different times, where different numbers of tasks were used as original tasks, while some were used as new tasks in the newly added tasks. All tasks were selected as the original tasks in the failure of agricultural machinery. The simulated results showed that in the case of newly added tasks, the improved NCP performed 0.83%-8.05% lower than the traditional CNP, while the number of communications with the server was reduced by 80%-85%. In the case of failure of agricultural machinery, the improved NCP performed 1.77%-12.89% lower than the traditional CNP, while the number of communications with the server was reduced by 77.4%. The simulated data demonstrated that the improved NCP behaved a much better performance on the multi-machine cooperative dynamic task allocation, compared with the traditional NCP. Finally, the seeding operational data of a farm was selected to verify in the Hinggan League of Inner Mongolia of western China. The operation day was selected with the case of newly added tasks. A multi-machine cooperative dynamic task allocation was also performed on the improved CNP at various moments of task allocation on the daily operation. A systematic analysis was made to compare the work time of that day before and after dynamic task assignment using the improved CNP. Specifically, the improved CNP reduced the cost by 30.20%-34.09% under different times of dynamic task allocation, indicating a better performance and higher efficiency in the dynamic task allocation for precision agricultural production.