{"title":"通信受限环境中多无人机系统的分布式任务分配方法","authors":"Shaokun Yan, Jingxiang Feng, Feng Pan","doi":"10.3390/drones8080342","DOIUrl":null,"url":null,"abstract":"This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.","PeriodicalId":507567,"journal":{"name":"Drones","volume":"77 19","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Distributed Task Allocation Method for Multi-UAV Systems in Communication-Constrained Environments\",\"authors\":\"Shaokun Yan, Jingxiang Feng, Feng Pan\",\"doi\":\"10.3390/drones8080342\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.\",\"PeriodicalId\":507567,\"journal\":{\"name\":\"Drones\",\"volume\":\"77 19\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Drones\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/drones8080342\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drones","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/drones8080342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文通过对新型启发式性能影响(PI)算法进行扩展,解决了在时间和通信受限环境下多无人机系统的任务分配问题。本文提出的算法被称为本地重新分配性能影响(LR-PI),由改进的任务包含阶段、新颖的通信和冲突解决阶段以及未分配任务的系统重新分配方法组成。考虑到合作完成任务可能需要多架无人机或单架无人机,任务包含阶段可以采用贪婪方法在每架无人机上建立有序的任务列表,并进一步降低任务的重要性值,最终实现无冲突分配。此外,未分配任务的本地重新分配侧重于最大化无冲突分配任务的数量。特别是,在冲突解决阶段,非理想通信因素,如比特误差、时间延迟和包丢失,与任务分配结合在一起,这些因素不可避免地存在,并会降低现实通信环境中的任务分配性能。最后,我们展示了所提算法在不同通信参数下的性能,并验证了其与 PI-MaxAsses 和基准 PI 算法相比的优越性。
A Distributed Task Allocation Method for Multi-UAV Systems in Communication-Constrained Environments
This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.