Abstract
Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic literature review of SR strategies that are applied to target search problems, so as to show which are being explored in the fields as well as the performance of current state-of-the-art SR approaches. This review extracts data from four scientific databases and filters with two established high-indexed databases (Scopus and Web of Science). Notably, 25 selected articles fell under two main categories in environment complexity, namely empty space and cluttered. There are four strategies which have been compiled for both empty space and cluttered categories, namely, bio-inspired mechanism, behavior-based mechanism, random strategy mechanism, and hybrid mechanism.
Highlights
The choice of keywords for building the search strings was based on terms that were commonly found in the literature and the term that was related to this review
This article covered the main papers of swarm robotics strategies that were applied to target search problem by systematically answering the research questions that had been described in the literature review planning protocol
The particle swarm optimization (PSO) algorithm was developed to focus on solving global optimization problems
Summary
SR strategies have continuously increased the attention in many applications, especially those that are 3D (dangerous, dirty, and dull) mission-related, such as search and rescue [19], pollution detection [20], and natural disaster monitoring [21] These types of applications require a large number of agents, are time consuming and may even be dangerous to a human being [22]. The problem of target searching has continued for a very long time, and more civilian applications have emerged This includes a wide variety of high-impact applications; for example, rescue operations in disaster areas, exploration for natural resources, environmental monitoring, and air surveillance. The organization of this paper is structured as follows: Section 2 describes the planning and execution of the SLR; Section 3 presents an overview and characteristics of SR; Section 4 gives a summary of the studied literature, delivers the answers to the research questions, and the explanation of the main characteristic of SR strategies that are taken in the target search problem; and in Section 5, the paper’s contributions are presented, and concluding remarks are summarized
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