Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints

Abstract

This paper tackles a class of multi-robot task allocation (MRTA) problems called “Single-Task Robots and Single-Robot Tasks” or SR–ST problems, subject to the following additional characteristics: tasks with deadlines, tasks that are generated during the mission, and robots with range and payload constraints (thus requiring multiple tours per robot). While these characteristics are typical of various disaster response and commercial applications, there is a lack of online MRTA solutions to address them. To solve this class of complex MRTA problems, an efficient online method (which is also suitable for decentralized deployment) is developed based on the construction and weighted matching of bipartite graphs. An exact integer linear programming (ILP) formulation of this class of MRTA problems is also developed, the solution of which serves both as an offline MRTA approach and as a provably optimal benchmark against which the online method is compared. The new methods are applied to a flood response problem where multiple unmanned aerial vehicles must respond to victims spread out over a large area. The results show that the new online algorithm closely trails the offline ILP method in terms of task completion performance, while being >103 times more computationally efficient compared to the ILP method. Dedicated case studies provide further insights into the favorable scalability of the online method with an increasing number of UAVs — offering up to 46% higher task completion compared to a random walk baseline in huge 1000-task problems. Lastly, application to a slightly different class of SR–ST problems and comparison of the ensuing results with that of corresponding state-of-the-art methods demonstrate the potential wider applicability of the proposed online MRTA method.