Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Prithviraj Dasgupta

Second Advisor

Dr. Sanjukta Bhowmick

Third Advisor

Dr. Jack Heidel

Fourth Advisor

Dr. Angelica Munoz-Melendez


Multi-Robot Task Allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to match a set of robots to a set of tasks so that the tasks can be completed by the robots while optimizing a certain metric such as the time required to complete all tasks, distance traveled by the robots and energy expended by the robots. We consider a scenario where the tasks can appear dynamically and the location of tasks are not known a priori by the robots. Additionally, for a task to be completed, it needs to be performed by multiple robots. This setting is called the MR-ST-TA (multi-robot, single-task, time- extended assginment) category of MRTA; solving the MRTA problem for this category is a known NP-hard problem. In this thesis, we address this problem by proposing a new algorithm that uses a spatial queue-based model to allocate tasks between robots while comparing its performance to several other known methods. We have implemented these algorithms on an accurately simulated model of Corobot robots within the Webots simulator for different numbers of robots and tasks. The results show that our method is adept in all proffered environments, especially scenarios that benefit from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits inefficient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an offline algorithm) fails to keep pace as the robot count increases.


A Thesis Presented to the Department of Computer Science and the Faculty of the Graduate College University of Nebraska In Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science. Copyright 2013 William H. Lenagh.