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Optimizing Parallel Task Execution for Multi-Agent Mission Planning
Publication Type:
Journal article
DOI:
10.1109/ACCESS.2023.3254900
Abstract
Multi-agent systems have received a tremendous amount of attention in many areas of research
and industry, especially in robotics and computer science. With the increased number of agents in missions,
the problem of allocation of tasks to agents arose, and it is one of the most fundamental classes of problems
in robotics, formally known as the Multi-Robot Task Allocation (MRTA) problem. MRTA encapsulates
numerous problem dimensions, and it aims at providing formulations and solutions to various problem
configurations, i.e., complex multi-agent missions. One dimension of the MRTA problem has not caught
much of the research attention. In particular, problem configurations including Multi-Task (MT) robots
have been neglected. However, the increase in computational power, in robotic systems, has allowed the
utilization of parallel task execution. This in turn had the benefit of allowing the creation of more complex
robotic missions; however, it came at the cost of increased problem complexity. Our contribution to the
aforementioned domain can be grouped into three categories. First, we model the problem using two
different approaches, Integer Linear Programming and Constraint Programming. With these models, we aim
at filling the gap in the literature related to the formal definition of MT robot problem configuration. Second,
we introduce the distinction between physical and virtual tasks and their mutual relationship in terms of
parallel task execution. This distinction allows the modeling of a wider range of missions while exploiting
possible parallel task execution. Finally, we provide a comprehensive performance analysis of both models,
by implementing and validating them in CPLEX and CP Optimizer on the set of problems. Each problem
consists of the same set of test instances gradually increasing in complexity, while the percentage of virtual
tasks in each problem is different. The analysis of the results includes exploration of the scalability of both
models and solvers, the effect of virtual tasks on the solvers’ performance, and overall solution quality.
Bibtex
@article{Miloradovic6651,
author = {Branko Miloradovic and Baran {\c{C}}{\"u}r{\"u}kl{\"u} and Mikael Ekstr{\"o}m and Alessandro Papadopoulos},
title = {Optimizing Parallel Task Execution for Multi-Agent Mission Planning},
volume = {11},
pages = {24367--24381},
month = {March},
year = {2023},
journal = {IEEE Access},
url = {http://www.es.mdu.se/publications/6651-}
}