Flocking behaviour in Multi-agent Systems (MAS) has attracted tremendous attention amongst researchers in the recent past due to its potential applications in various fields where distributed work environment is desired. The flocking algorithms have the potential to introduce self-organizing, self-healing and selfconfiguring capabilities in the functioning of a distributed system. The flocking ...

Flocking is a term that describes the behavior of a group of birds (a “flock”) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems ‘’Boids’’such as ground systems (robotic vehicles/ swarm robots). Each one of these systems collectively moves inside/outside of a buildin...

in previous papers authors have considered agents as inertia-less self driven particles and designed a flocking algorithm. application of this algorithm to agents with considerable inertial characteristics needs a behavioural controller. the controller uses the local information and helps every agent to imitate the desired behaviour as a member of the flocking frame which covers the main issue ...

We study the problem of flocking and velocity alignment in a group of kinematic nonholonomic agents in 2 and 3 dimensions. By analyzing the velocity vectors of agents on a circle (for planar motion) or sphere (for 3D motion), we develop a geodesic control law that minimizes a misalignment potential and results in velocity alignment and flocking. The proposed control laws are distributed and wil...

In flocking, a large number of individuals move cohesively in a common direction. Many examples can be found in nature: from simple organisms such as crickets and locusts to more complex ones such as birds, fish and quadrupeds. Reynolds was the first to propose a computational model of flocking [1]. The behavior of each individual is made of three parts: separation, cohesion, alignment. Separat...

In previous papers authors have considered agents as inertia-less self driven particles and designed a flocking algorithm. Application of this algorithm to agents with considerable inertial characteristics needs a behavioural controller. The controller uses the local information and helps every agent to imitate the desired behaviour as a member of the flocking frame which covers the main is...

In this study, a novel procedure is presented for control and analysis of a group of autonomous agents with point mass dynamics achieving flocking motion by using a fuzzy-logic-based attractive/repulsive function. Two cooperative control laws are proposed for a group of autonomous agents to achieve flocking formations related to two different centers (mass center and geometric center) of the fl...

In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses more elaborate sensing mechanisms to obtain th...