Maintaining the spatial stability of a swarm of autonomous unmanned aerial vehicles

This paper describes the application of a swarm engineering methodology that allows creating hexagonal UAV grids with predefined properties. It is achieved by imitation of physics processes that demonstrate conditions for stabilizing the above-mention hexagon grids of UAV swarm. We propose a simple combination of software and hardware applications that create a more efficient practical solution.

Mimicking Sub-Structures Self-Organization in Microtubules

Microtubules (MTs) are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta globular proteins constituting the heterodimer building blocks combine to form these tubules through polymerization, controlled by the concentration of Guanosine-triphosphate (GTPs) and other Microtubule Associated Proteins (MAPs). MTs play a crucial role in many intracellular processes, predominantly in mitosis, organelle transport and cell locomotion. Current research in this area is focused on understanding the exclusive behaviors of self-organization and their association with different MAPs through organized laboratory experiments. However, the intriguing intelligence behind these tiny machines resulting in complex self-organizing structures is mostly unexplored. In this study, we propose a novel swarm engineering framework in modeling rules for these systems, by combining the principles of design with swarm intelligence. The proposed framework was simulated on a game engine and these simulations demonstrated self-organization of rings and protofilaments in MTs. Analytics from these simulations assisted in understanding the influence of GTPs on protofilament formation. Also, results showed that the population density of GTPs rather than their bonding probabilities played a crucial role in polymerization in forming microtubule substructures.

Swarm Engineering Through Quantitative Measurement of Swarm Robotic Principles in a 10,000 Robot Swarm

When designing swarm-robotic systems, system- atic comparison of algorithms from different do- mains is necessary to determine which is capa- ble of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed met- rics as a design tool by solving a large object gath- ering problem in temporally varying operating con- ditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.

V-Shaped Formation Control for Robotic Swarms Constrained by Field of View

By forming a specific formation during motion, the robotic swarm is a good candidate for unknown region exploration applications. The members of this kind of system are generally low complexity, which limits the communication and perception capacities of the agents. How to merge to the desired formation under those constraints is essential for performing relevant tasks. In this paper, a limited visual field constrained formation control strategy inspired by flying geese coordinated motion is introduced. Usually, they flock together in a V-shape formations, which is a well-studied phenomenon in biology and bionics. This paper illustrates the proposed methods by taking the research results from the above subjects and mapping them from the swarm engineering point of view. The formation control is achieved by applying a behavior-based formation forming method with the finite state machine while considering anti-collision and obstacle avoidance. Furthermore, a cascade leader–follower structure is adopted to achieve the large-scale formations. The simulation results from several scenarios indicate the presented method is robust with high scalability and flexibility.