In this paper, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation. Previous work includes controller-based algorithms, swarm-based algorithms, and potential-field-based algorithms. They usually focus on only one or the other of these tasks, solve the problem greedily without systematic search, and thus generate costly solutions or even fail to find solutions in congested environment. In this paper, we develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it.
Swarm intelligence is a natural step in the evolution of certain social species. It explains why ants colonize, bees swarm, fish school and birds flock. Nature has proven that when individual creatures collaboratively work and think together as unified systems toward a common goal, they're more likely to reach that goal faster and more accurately than if they were to attempt it individually. In other words, they're smarter together than they are on their own. Swarm intelligence is the collective behavior of decentralized, self-organized systems (natural or artificial) that can maneuver quickly in a coordinated fashion. In nature, this closed-loop, collaborative behavior is unique within each species.
Swarm robotics is a relatively new and highly promising research field, which entails the development of multi-robot teams that can move and complete tasks together. Robot swarms could have numerous valuable applications. For instance, they could support humans during search and rescue missions or allow them to monitor geographical areas that are difficult to access. Researchers at Fraunhofer FKIE and University of Bonn in Germany have recently devised a theoretical construct that could guide the development of self-organizing human-swarm systems. This construct, presented in a paper published in Sage's Adaptive Behavior journal, provides a new holistic perspective to human-swarm interaction, which the team refers to as "joint human-swarm loops."
This blog is a summary of an article written by Fortinet's Derek Manky that appeared on the ThreatPost website on January 31, 2019. The digital world has created unprecedented opportunities – both for good and for ill. Advances in swarm technology, for example, have powerful implications in the fields of medicine, transportation, engineering, and automated problem solving. However, if used maliciously, it may also be a game changer for the bad guys if organizations don't update their security strategies. For example, a new methodology reproduces natural swarm behaviors to control clusters of nano-robots, which can then be directed to perform precise structural changes with a high degree of reconfigurability, such as extending, shrinking, splitting, and merging.
This paper proposes a tutorial on the Data Clustering technique using the Particle Swarm Optimization approach. Following the work proposed by Merwe et al. here we present an in-deep analysis of the algorithm together with a Matlab implementation and a short tutorial that explains how to modify the proposed implementation and the effect of the parameters of the original algorithm. Moreover, we provide a comparison against the results obtained using the well known K-Means approach. All the source code presented in this paper is publicly available under the GPL-v2 license.