Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

摘要

ABSTRACT Swarm technology is evolving quickly in the new era in the domains of autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). Swarm technology takes its cues from nature, particularly from the behaviors of ants, bees, schools of fish, and birds. The rapid growth of swarm technologies can be attributed to this organic inspiration. It is utilized in a variety of applications, such as pick‐and‐place robotics and drone swarms for laser shows. To mimic the behavior of swarm robots or microbots, this research work explores the use of swarm algorithms, namely particle swarm optimization (PSO) and rendezvous algorithms (RA). The emphasis will be on applying these algorithms to situations that require swarm robots to navigate around obstacles while planning their paths and forming patterns. This study shows how successful these algorithms are by using MATLAB as a useful tool. The outcomes demonstrate that the algorithms are capable of producing intricate patterns and facilitating effective path planning for swarms of robots. The robots' ability to avoid obstacles and travel around them at a high speed is noteworthy. This study covers a wide range of industries, such as space exploration, military/defense applications, storage, surveillance, and search and rescue. The application of swarm technology demonstrates how it may transform practical situations and provides information about how flexible and efficient swarm algorithms are in a variety of settings. By offering useful insights for researchers, engineers, and practitioners in utilizing the combined potential of swarm intelligence (SI) for a variety of applications, this investigation advances the field of swarm robotics.