Complex logistics support missions require the execution of spatially separated information gathering and situational awareness tasks. Mobile robot teams can play an important role in the automated execution of these tasks to reduce mission completion time. Planning strategies for such missions must take into account the formation of effective coalitions among available robots and assignment of tasks to robots with the goal of minimizing the expected mission completion time. The occurrence of unexpected situations that adversely interfere with the execution of the mission may require the execution of contingency tasks so that the originally planned tasks may proceed with minimal disruption. Initially reported potential contingency tasks may not always affect mission tasks due to the uncertainty in the mission environment. When potential contingency tasks are reported, the planner updates its existing plan to minimize the expected mission completion time based on the probability of these contingency tasks impacting the mission, their impact on the mission, and other task characteristics. We describe various heuristic-based strategies to compute task allocations for robots for mission execution. We perform simulation experiments to compare them and analyze the computational performance of the best performing strategy. We show that the proactive approach to contingency task management outperforms both the conservative and reactive approaches. Note to Practitioners —The work reported in this article will be useful for deploying multirobot teams to support complex logistics missions spread over a large area where the robots must be prepared to handle contingencies that can adversely impact the mission. The proposed proactive approach can be used to handle contingencies in information gathering, surveillance, guarding, and situational awareness tasks to support safe and secure transportation of important assets through crowded areas. We use port operation as an illustrative example where unmanned surface and aerial vehicles can be useful in ensuring the safety and security of the ports. This is a computationally challenging problem. This article proposes heuristic algorithms to solve the task allocation problem among many different agents efficiently. The approach presented in this article integrates the available information regarding mission and contingencies, along with the resource constraints to plan the mission execution.

中文翻译：

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将应急任务纳入多机器人团队的任务分配中

复杂的后勤保障任务需要执行空间分隔的信息收集和态势感知任务。移动机器人团队可以在这些任务的自动化执行中发挥重要作用，以减少任务完成时间。此类任务的计划策略必须考虑到可用机器人之间的有效联盟的形成以及将任务分配给机器人的目的，以最大程度地减少预期的任务完成时间。发生不利地影响任务执行的意外情况可能需要执行应急任务，以使原先计划的任务可以在最小的干扰下进行。最初报告潜在由于任务环境的不确定性，应急任务可能并不总是会影响任务任务。当报告了潜在的应急任务时，计划人员将根据这些应急任务影响任务的概率，任务对任务的影响以及其他任务特征，更新其现有计划，以最大程度地缩短预期的任务完成时间。我们描述了各种基于启发式的策略来计算机器人的任务执行分配。我们进行仿真实验以进行比较，并分析性能最佳的策略的计算性能。我们表明，应急任务管理的主动方法优于保守方法和被动方法。执业者注意 —本文报道的工作对于部署多机器人团队以支持遍布整个区域的复杂后勤任务很有用，在该区域中，必须做好准备以应对可能会对任务产生不利影响的突发事件的机器人。所提议的主动方法可用于处理信息收集，监视，防护和态势感知任务中的突发事件，以支持重要资产通过拥挤区域的安全运输。我们以港口操作为例，无人水上飞机和航空器可用于确保港口的安全性。这是一个计算难题。本文提出了启发式算法来有效解决许多不同智能体之间的任务分配问题。