Abstract
Particle swarm optimization (PSO), a new population-based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area. Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time. This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star. To validate the effectiveness and usefulness of algorithms, a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.
Highlights
A behaviour-based paradigm has had a strong impact on multi-robot system research
This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obsta‐ cles
The first problem of basic Particle swarm optimization (PSO) is premature convergence, which appeared in this domain when there are static obstacles in the search space and the initial positions of the robots are far from the target
Summary
A behaviour-based paradigm has had a strong impact on multi-robot system research. The social characteristics of insects and animals are analysed in order to examine and apply these findings in designing multi-robot systems. Researchers have proposed several methods to solve this problem in different domains [17, 18] This drawback is evident in multi-robot search systems designed on basic PSO. In some cases, the robots are placed near the target but have to move based on the velocity and position equations of basic PSO, which may guide the robots to move to the position located farther from the target This situation obviously causes the search time to increase, when there are obstacles near the target. A new method is proposed on a multirobot search system which increases the global searching and guides the robots to escape from the local optima and explore different areas to find the desired target. There are some key differ‐ ences between PSO and PSO in the multi-robot search that requires us to make some modifications to the algorithm
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