Comparative Analysis of Various Optimization Techniques for Solving Multi-Robot Task Allocation Problem

Nowadays, The dependency on robotic fleets is increasing all over the globe. As a result of this increase, the Multi-Robot Systems (MRS) become a topic of considerable interest. One of the most problems solved by the introduction of MRS is the Multi-Robot Task Allocation (MRTA) problem. In order to determine the most suitable technique used in solving the MRTA problem, optimization based approaches are investigated. This paper represents a guide for researchers in the field of MRTA application to choose the suitable algorithm to solve the problem depending on the problem space and constraints. This paper introduces two different stochastic approaches to solve such problem which are the Genetic Algorithm (GA) and the Ant-Colony Optimization (ACO) algorithm. The two algorithms are tested and compared through several test cases. Results show that both algorithms have acceptable performance in terms of minimum distance and time convergence with certain limitations for each algorithm that are discussed through out the study.