IEEE Trans Cybern. 2016 Apr;46(4):902-15. doi: 10.1109/TCYB.2015.2418052. Epub 2015 Apr 13.
Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle's task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm.
使用分布式任务分配方法来管理协同多车辆系统正变得越来越有吸引力。然而，大多数努力都集中在各种特定的实验工作上，而对于感兴趣的问题和现有方法的系统分析却做得很少。在本文中，首先根据搜索和救援场景提出了一种通用的场景描述和系统配置。然后，分析了问题的目标，并从场景中提取了其数学公式。考虑到分布式计算的要求，本文随后提出了一种用于多车辆多任务分配问题的新颖启发式分布式任务分配方法。所提出的方法简单有效。它直接针对为问题定义的数学目标进行优化。为每个任务定义了一个新的重要性概念，并通过车辆生成的局部成本的贡献来衡量，这是算法的关键思想的基础。整个算法在任务包含阶段和一致性与任务删除阶段之间迭代，在所有存在本地通信的车辆上并行运行。前一阶段用于将任务包含到车辆的任务列表中，以优化整体目标，而后一阶段用于就车辆的任务重要性达成共识，并删除已分配给其他车辆的任务。数值模拟表明，所提出的方法能够提供无冲突的解决方案，并在与基于共识的束算法的比较中表现出色。
