Abstract
The swarms of robots are examples of artificial collective intelligence, with simple individual autonomous behavior and emerging swarm effect to accomplish even complex tasks. Modeling approaches for robotic swarm development is one of the main challenges in this field of research. Here, we present a robot-instantiated theoretical framework and a quantitative worked-out example. Aiming to build up a general model, we first sketch a diagrammatic classification of swarms relating ideal swarms to existing implementations, inspired by category theory. Then, we propose a matrix representation to relate local and global behaviors in a swarm, with diagonal sub-matrices describing individual features and off-diagonal sub-matrices as pairwise interaction terms. Thus, we attempt to shape the structure of such an interaction term, using language and tools of quantum computing for a quantitative simulation of a toy model. We choose quantum computing because of its computational efficiency. This case study can shed light on potentialities of quantum computing in the realm of swarm robotics, leaving room for progressive enrichment and refinement.
Highlights
In a swarm of robots [3,4,5], every single robot is performing simple tasks and showing a simple behavior, but the interaction and information exchange amongst robots allows the accomplishment of more complex tasks, impossible to be achieved by a single unit
We proposed a matrix description of a generic swarm, trying to connect the action of each single robot with the overall behavior
The proposed swarm matrix is a block matrix with sub-matrices indicating the motion of single robots, and off-diagonal blocks representing pairwise interaction terms, with signals from the i-th robot and behavioral response for the j-th robot
Summary
Acrobatics evolution of flocks of birds in our skies and colorful movements of schools of fish in our oceans inspire poets and artists, as well as computer scientists and engineers.Complex behaviors exhibited by swarms of animals [1,2], often inspire the development of swarms of robots, passing through toy models which are progressively enriched and refined [3].In a swarm of robots [3,4,5], every single robot is performing simple tasks and showing a simple behavior, but the interaction and information exchange amongst robots allows the accomplishment of more complex tasks, impossible to be achieved by a single unit.Typically, the collaboration in a swarm is decentralized: there is no such a thing as a “robot leader;” instead, every single robot is acting autonomously, reacting to the information received by its neighbors, and transmitting information about its own activity. Complex behaviors exhibited by swarms of animals [1,2], often inspire the development of swarms of robots, passing through toy models which are progressively enriched and refined [3]. In a swarm of robots [3,4,5], every single robot is performing simple tasks and showing a simple behavior, but the interaction and information exchange amongst robots allows the accomplishment of more complex tasks, impossible to be achieved by a single unit. Swarms of robots show the emergence of global behavior and a form of collective intelligence—a classic example is given by ants’ behavior. Examples of swarm intelligence are stochastic diffusion search [7], ant colony optimization [8], and artificial swarm intelligence [9]
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