Systematic literature review of swarm robotics strategies applied to target search problem with environment constraints

• 发表在: Applied Sciences (Switzerland)
• 主要作者: 2-s2.0-85102921294
• 格式: Review
• 语言: English
• 出版: MDPI AG 2021
• 在线阅读: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102921294&doi=10.3390%2fapp11052383&partnerID=40&md5=d26fe1a2b17aa012e8d9e3d62e2a5859

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.