Affiliations: [a] Department of Computer Science, Iqra University, Karachi, Pakistan | [b] Artificial Intelligence Lab, Institute of Business Administration, Karachi, Pakistan
Correspondence:
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Corresponding author: Muhammad Usman Arif, Department of Computer Science, Iqra University, Karachi, Pakistan. Tel.: +9221 111 114 772, ext: 1720; E-mail: dr.usmanarif@iqra.edu.pk.
Abstract: Multi-Robot Task Allocation (MRTA) is a complex problem domain with the majority of problem representations categorized as NP-hard. Existing solution approaches handling dynamic MRTA scenarios do not consider the problem structure changes as a possible system dynamic. RoSTAM (Robust and Self-adaptive Task Allocation for Multi-robot teams) presents a novel approach to handle a variety of MRTA problem representations without any alterations to the task allocation framework. RoSTAM’s capabilities against a range of MRTA problem distributions have already been established. This paper further validates RoSTAM’s performance against the more conventional dynamics, such as robot failure and new task arrival, while performing allocations against two of the most frequently faced problem representations. The framework’s performance is evaluated against a state-of-the-art online auction scheme. The results validate RoSTAM’s capability to allocate tasks across a range of dynamics efficiently.
