Swarm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1] , [2] . Typically, swarm robotics extracts engineering principles from the study of those natural systems in order to provide multirobot systems with comparable abilities. This way, it aims to build systems that are more robust, fault-tolerant, and flexible than single robots and that can better adapt their behavior to changes in the environment.

/pdf/swarm-robotics-past-present-and-future-574s2y78xg.pdf

Swarm Robotics: Past, Present, and Future

The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making problem dealt with by the swarm. We find that, so far, the literature has primarily focused on certain variants of the best-of-n problem while other variants have been the subject of only a few isolated studies. Additionally, we consider a second taxonomy about the design methodologies used to develop collective decision-making strategies. Based on this second taxonomy, we provide an in-depth survey of the literature that details the strategies proposed so far and discusses the advantages and disadvantages of current design methodologies.

/pdf/the-best-of-n-problem-in-robot-swarms-formalization-state-of-2wh5koftxl.pdf

The Best-of-n Problem in Robot Swarms: Formalization, State of the Art, and Novel Perspectives

Plasticity of cancer cell invasion: Patterns and mechanisms

The concerted responses of eusocial insects to environmental stimuli are often referred to as collective cognition at the level of the colony. To achieve collective cognition, a group can draw on two different sources: individual cognition and the connectivity between individuals. Computation in neural networks, for example, is attributed more to sophisticated communication schemes than to the complexity of individual neurons. The case of social insects, however, can be expected to differ. This is because individual insects are cognitively capable units that are often able to process information that is directly relevant at the level of the colony. Furthermore, involved communication patterns seem difficult to implement in a group of insects as they lack a clear network structure. This review discusses links between the cognition of an individual insect and that of the colony. We provide examples for collective cognition whose sources span the full spectrum between amplification of individual insect cognition and emergent group-level processes.

https://hal.inria.fr/hal-01432718/document

Individual versus collective cognition in social insects.

Swarms of low-cost autonomous robots can potentially be used to collectively perform tasks over very large domains and time scales. Novel robots for swarm applications are currently being developed as a result of recent advances in sensing, actuation, processing, power, and manufacturing. These platforms can be used by researchers to conduct experiments with robot collectives and by educators to include robotic hardware in their curricula. However, existing low-cost robots are specialized and can lack desired sensing, navigation, control, and manipulation capabilities. This letter presents a new mobile robot platform, Pheeno, that is affordable, versatile, and suitable for multirobot research, education, and outreach activities. Users can modify Pheeno for their applications by designing custom modules that attach to its core module. We describe the design of the Pheeno core and a three degree-of-freedom gripper module, which enables unprecedented manipulation capabilities for a robot of Pheeno's size and cost. We experimentally demonstrate Pheeno's ability to fuse measurements from its onboard odometry for global position estimation and use its camera for object identification in real time. We also show that groups of two and three Pheenos can act on commands from a central controller and consistently transport a payload in a desired direction.

/pdf/pheeno-a-versatile-swarm-robotic-research-and-education-1l92jt0d3t.pdf

Pheeno, A Versatile Swarm Robotic Research and Education Platform

The nests of social insects are not only impressive because of their sheer complexity but also because they are built from individuals whose work is not centrally coordinated. A key question is how groups of insects coordinate their building actions. Here, we use a combination of experimental and modeling approaches to investigate nest construction in the ant Lasius niger. We quantify the construction dynamics and the 3D structures built by ants. Then, we characterize individual behaviors and the interactions of ants with the structures they build. We show that two main interactions are involved in the coordination of building actions: (i) a stigmergic-based interaction that controls the amplification of depositions at some locations and is attributable to a pheromone added by ants to the building material; and (ii) a template-based interaction in which ants use their body size as a cue to control the height at which they start to build a roof from existing pillars. We then develop a 3D stochastic model based on these individual behaviors to analyze the effect of pheromone presence and strength on construction dynamics. We show that the model can quantitatively reproduce key features of construction dynamics, including a large-scale pattern of regularly spaced pillars, the formation and merging of caps over the pillars, and the remodeling of built structures. Finally, our model suggests that the lifetime of the pheromone is a highly influential parameter that controls the growth and form of nest architecture.

https://www.pnas.org/content/pnas/113/5/1303.full.pdf

Stigmergic construction and topochemical information shape ant nest architecture

Lymphocytes alternate between phases of individual migration across tissues and phases of clustering during activation and function. The range of lymphocyte motility behaviors and the identity of the factors that govern them remain elusive. To explore this point, we here collected unprecedented statistics pertaining to cell displacements, cell:matrix and cell:cell interactions using a model B cell line as well as primary human B lymphocytes. At low cell density, individual B lymphocytes displayed a high heterogeneity in their speed and diffusivity. Beyond this intrinsic variability, B lymphocytes adapted their motility to the composition of extra-cellular matrix, adopting slow persistent walks over collagen IV and quick Brownian walks over fibronectin. At high cell density, collagen IV favored the self-assembly of B lymphocytes into clusters endowed with collective coordination, while fibronectin stimulated individual motility. We show that this behavioral plasticity is controlled by acto-myosin dependent adhesive and Arp2/3-dependent protrusive actin pools, respectively. Our study reveals the adaptive nature of B lymphocyte motility and group dynamics, which are shaped by an interplay between and cell:matrix and cell:cell interactions.

/pdf/switching-between-individual-and-collective-motility-in-b-41wtrjae50.pdf

Switching between individual and collective motility in B lymphocytes is controlled by cell-matrix adhesion and inter-cellular interactions.

Interactions between individuals and the structure of their environment play a crucial role in shaping self-organized collective behaviors. Recent studies have shown that ants crossing asymmetrical bifurcations in a network of galleries tend to follow the branch that deviates the least from their incoming direction. At the collective level, the combination of this tendency and the pheromone-based recruitment results in a greater likelihood of selecting the shortest path between the colony's nest and a food source in a network containing asymmetrical bifurcations. It was not clear however what the origin of this behavioral bias is. Here we propose that it results from a simple interaction between the behavior of the ants and the geometry of the network, and that it does not require the ability to measure the angle of the bifurcation. We tested this hypothesis using groups of ant-like robots whose perceptual and cognitive abilities can be fully specified. We programmed them only to lay down and follow light trails, avoid obstacles and move according to a correlated random walk, but not to use more sophisticated orientation methods. We recorded the behavior of the robots in networks of galleries presenting either only symmetrical bifurcations or a combination of symmetrical and asymmetrical bifurcations. Individual robots displayed the same pattern of branch choice as individual ants when crossing a bifurcation, suggesting that ants do not actually measure the geometry of the bifurcations when travelling along a pheromone trail. Finally at the collective level, the group of robots was more likely to select one of the possible shorter paths between two designated areas when moving in an asymmetrical network, as observed in ants. This study reveals the importance of the shape of trail networks for foraging in ants and emphasizes the underestimated role of the geometrical properties of transportation networks in general.

/pdf/do-ants-need-to-estimate-the-geometrical-properties-of-trail-32editrmo7.pdf

Do ants need to estimate the geometrical properties of trail bifurcations to find an efficient route? A swarm robotics test bed.

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Stigmergic construction and topochemical information shape ant nest architecture

Switching between individual and collective motility in B lymphocytes is controlled by cell-matrix adhesion and inter-cellular interactions.

Do ants need to estimate the geometrical properties of trail bifurcations to find an efficient route? A swarm robotics test bed.