SymSwarm: A Two-Staged Decentralized Symmetrical Pattern Formation Paradigm for a Swarm of Mobile Robots

For several decades, cooperative control of autonomous mobile robots has been an active research area with potential applications in dynamic areas such as agriculture, industrial plants, and the military. Although numerous research studies on pattern formation using multiple mobile agents have been conducted, no algorithm for symmetric pattern formation that is independent of the number of agents and prioritizes user control has been introduced. This article demonstrates an attempt to manifest an end-to-end symmetrical pattern in a swarm of mobile robots. Here, a two-staged paradigm named SymSwarm is developed, which incorporates the novelty of forming symmetrical formations around a point within a coveted area via decentralized communication among the swarm. SymSwarm is built on a novel concept of a bio-inspired state model called compete and point state model (CPSM), which enables smarter and more robust computation and self-regulation. While most bio-inspired models focus on the characteristics of bees, ants, cats, chimpanzees, and a variety of other animals, this paradigm focuses on the competitive sentiment and leadership ability of human beings to shape the CPSM mentioned above. The algorithm for acquiring symmetrical points is developed using the circle polygon analogy in Cartesian geometry. The following sections propose a decentralized communication mechanism for adjacent consecutive robots as well as a heuristic nearest point finder algorithm. The proposed paradigm is evaluated using robotics simulator (V-REP), and the results indicate that it is consistently successful and has a low rate of pattern formation error in the presence or absence of obstacles in the environment.