Abstract
This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time.
Highlights
In the last few years, the field of research in mobile robotics has encountered a significant shift as the researchers in this field have recently started focusing on multirobot systems (MRS) rather than single-robot systems
This increased interest in the community of mobile robotics research towards MRS comes from the significant advantages and higher potential provided by MRS than single-robot systems
This paper presented a comparative study between two wellknown approaches, metaheuristic-based and market-based approaches, that are used extensively to solve the Multirobot task allocation (MRTA) problem
Summary
In the last few years, the field of research in mobile robotics has encountered a significant shift as the researchers in this field have recently started focusing on MRS rather than single-robot systems. One of the main areas of research in this field is the task allocation problem in MRS, where the mapping of robots to tasks is done in order to increase the overall performance of the system. The task allocation problem is a major issue in MRS as it focuses on the proper utilization of the available resource. In MRS, the available resources are the robots which are used to solve a problem or to perform a certain task. In order to increase the performance of the system, one must efficiently utilizes the available robots in order to solve the required tasks.
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