# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 5 Limitations of Swarm Intelligence-Based Multi-Robotics  
While swarm intelligence-based multi-robotic systems offer significant potential, they also face substantial limitations. These limitations can hinder their performance and deployment in various real-world applications. Addressing these limitations requires ongoing research and development. Below are some key limitations and the current trends and future work to address them:  
* Limited Individual Capability: Swarm robots often have constraints on their sensing, processing, and action capabilities. This limited capability can restrict the types of tasks they can perform and the environments in which they can operate effectively. Regarding this limitation, current trends aim to enhance individual robot capabilities through advances in sensor technology, onboard processing power, and more efficient actuation systems. Future work includes integrating AI and machine learning to enable better decision-making and more sophisticated behaviors. * Complexity of Algorithm Design: Developing reliable algorithms that produce the desired collective behaviors from simple individual rules is highly challenging. Ensuring these robust, efficient, and adaptable algorithms is a significant hurdle. For this issue, research focuses on bio-inspired algorithms, such as those mimicking ant foraging or bird flocking. Machine learning, particularly reinforcement learning, is being explored to develop algorithms to learn optimal behaviors. Future work includes creating more modular and reusable algorithmic frameworks. * Unpredictability: Due to the complexity and non-linearity of interactions among robots, swarm systems can exhibit unpredictable behaviors. This unpredictability can lead to undesired emergent outcomes that are difficult to foresee and control. Efforts are being made to better understand and model emergent behaviors through simulations and theoretical analysis. Future research aims to develop control mechanisms to mitigate undesirable emergent behaviors and enhance predictability. * Dependence on Local Information: Swarm robots typically rely on local information and interactions to make decisions, which can lead to suboptimal global performance if local data are insufficient or misleading. Enhancing the ability of robots to share information and make more informed decisions based on a combination of local and global data. Future work includes developing hybrid approaches that balance local autonomy with occasional global coordination. * Interference and Congestion: High robot density can lead to interference and congestion, reducing the overall efficiency of the swarm. This is particularly problematic in confined or resource-limited environments. Developing advanced path planning and collision avoidance algorithms to minimize interference. Future research explores dynamic density management strategies where robots can adapt their density based on task requirements and environmental conditions. * Difficulty in Real-World Implementation: Translating theoretical models and simulations of swarm intelligence to practical, real-world systems is challenging. Environmental unpredictability, hardware limitations, and real-time constraints complicate implementation. Bridging the gap between theory and practice through extensive field testing and iterative development. Future work includes creating more robust and versatile robotic platforms that can operate reliably in diverse real-world conditions. * Maintenance and Scalability Costs: Large-scale deployment of swarm robotic systems can be financially demanding due to the costs associated with manufacturing, maintenance, and scalability. This includes not only the initial investment but also ongoing operational expenses. Reducing costs through advances in manufacturing technologies, such as 3D printing and modular design. Developing maintenance strategies that leverage the swarm's inherent redundancy and fault tolerance. Future work includes exploring economic models that make large-scale deployment more viable.