Abstract: Swarm intelligence, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms, helps robot swarms solve complex tasks through self-organization and localized interactions. It is a dynamic model that is scalable and adaptable to changes. These algorithms assist in various tasks, such as environmental navigation, path planning optimization, and task allocation. Logistics with warehouse robots streamlining inventory, agriculture via precision-farming ground swarms, disaster response using heterogeneous teams for mapping hazardous zones, and environmental monitoring with aquatic drones tracking pollution are all use cases on this model. We also address its limitations and future research directions, such as the integration of advanced AI and bio-hybrid systems. The presented analysis is supported by five detailed case studies and comparative tables. We also implemented and tested simulation results from select SI models on a "Pob-Bot" robot with an optimized hardware design.

Keywords: AI, cognitive AI, Particle Swarm Intelligence, Mobile Robots, Distributed Systems, Ant Colony Optimization

Pages: 310-316