Incorporation of Contingency Tasks in Task Allocation for Multirobot Teams

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.