# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 1 Introduction  
Swarm Intelligence (SI) is a burgeoning field within artificial intelligence that draws inspiration from the collective behavior observed in social insects such as ants, bees, and termites [1, 2, 3]. When applied to multi-robotics, SI facilitates the development of systems where numerous simple robots collaborate to perform complex tasks, exhibiting behaviors far more sophisticated than those of individual robots [4, 5]. This decentralized approach emphasizes self-organization, local interactions, and emergent behavior, allowing robotic swarms to adapt dynamically to their environment and accomplish tasks efficiently. The study of SI in multi-robotics aims to harness these natural principles to create systems that are not only efficient but also resilient and scalable. The motivation behind exploring SI in multi-robotics stems from its potential to revolutionize various industries and applications [6, 7, 8]. Traditional robotics often rely on centralized control systems, which can be bottlenecked by single points of failure and limited scalability. In contrast, SI offers robust, scalable, and flexible solutions operating in diverse and dynamic environments. The ability of swarms to self-organize and perform collective tasks without central oversight makes them ideal for applications such as search and rescue missions, environmental monitoring, agricultural automation, space exploration, and warehouse management [9, 10, 11, 12]. These systems can perform complex tasks autonomously, adapt to new challenges, and continue operation even if individual robots fail. This inherent robustness and adaptability present a significant advantage over traditional robotic systems. The research question that guides this review is: \"How effective is Swarm Intelligence in enabling multi-robotic systems to perform critical tasks, and what are the key challenges and limitations in such applications?\"  
The specific objectives of this review are as follows:  
* To provide an overview of the foundational principles and algorithms of Swarm Intelligence, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and other relevant algorithms used in multi-robotic systems. * To examine the application of SI in key domains, such as search and rescue, environmental monitoring, agriculture, and space exploration. * To identify and discuss the primary challenges and risks of using SI in multi-robot critical applications, focusing on areas like real-time decision-making, communication failures, and emergent behavior unpredictability. * To propose potential future research directions and strategies for mitigating the identified risks, thereby improving the reliability and effectiveness of SI in multi-robotic systems for critical tasks. The key focus of this study is a comprehensive review of SI, with a particular emphasis on its application to multi-robotics. By collecting studies from reputable sources (IEEE, Springer, ACM, and so on), this research aims to thoroughly understand the current advancements, challenges, and future directions in swarm intelligence and its implementation in multi-robot systems. In particular, our methodology for conducting this review is outlined below. * Search strategy: Collect studies from reputation sources, including IEEE Xplore, SpringerLink, ScienceDirect, and Google Scholar, using search terms such as \"swarm intelligence\", \"multi-robotics\", \"robotic swarms\", \"SI algorithms\", \"decentralized control\", and \"multi-agent systems\". The inclusion criteria focused on peer-reviewed journal articles, conference papers, and significant book chapters published within the last 15 years (2008-2023) to ensure coverage of foundational research and recent advancements. We excluded non-English articles, non-peer-reviewed content, and papers not explicitly addressing the intersection of SI and multi-robotics. * Selection: An initial screening of titles and abstracts was conducted to eliminate irrelevant studies, reducing the pool from over 200 articles to approximately 90.