Self-organized flocking in mobile robot swarms

In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots. We propose a behavior based on heading alignment and proximal control that is capable of generating self-organized flocking in a swarm of Kobots. By self-organized flocking we mean that a swarm of mobile robots, initially connected via proximal sensing, is able to wander in an environment by moving as a coherent group in open space and to avoid obstacles as if it were a “super-organism”. We propose a number of metrics to evaluate the quality of flocking. We use a default set of behavioral parameter values that can generate acceptable flocking in robots, and analyze the sensitivity of the flocking behavior against changes in each of the parameters using the metrics that were proposed. We show that the proposed behavior can generate flocking in a small group of physical robots in a closed arena as well as in a swarm of 1000 simulated robots in open space. We vary the three main characteristics of the VHS, namely: (1) the amount and nature of noise in the measurement of heading, (2) the number of VHS neighbors, and (3) the range of wireless communication. Our experiments show that the range of communication is the main factor that determines the maximum number of robots that can flock together and that the behavior is highly robust against the other two VHS characteristics. We conclude by discussing this result in the light of related theoretical studies in statistical physics.     

Cooperative navigation in robotic swarms

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.     

Self-organized and highly ordered domain structures within swarms of Myxococcus xanthus

Coordinated group movement (swarming) is a key aspect of Myxococcus xanthus' social behavior. Here we report observation of domain structures formed by multiple cells within large three-dimensional swarming groups grown on amorphous glass substrates, using the atomic force microscope (AFM). Novel analyses revealed that 90% of the wild type swarms displayed some form of preferential cell alignment. In contrast, cells with mutations in the social and adventurous motility systems displayed a distinct lack of cell alignment. Video microscopy observations of domain features of in vivo swarming M. xanthus cells were also consistent with the AFM data. The results presented here reveal that unique domain formation within swarms of wild type cells is a biologically driven process requiring the social and adventurous motility systems and is not a statistical phenomenon or thermodynamic process arising from liquid crystal behavior.     

Design of ant-inspired stochastic control policies for collective transport by robotic swarms

In this paper, we present an approach to designing decentralized robot control policies that mimic certain microscopic and macroscopic behaviors of ants performing collective transport tasks. In prior work, we used a stochastic hybrid system model to characterize the observed team dynamics of ant group retrieval of a rigid load. We have also used macroscopic population dynamic models to design enzyme-inspired stochastic control policies that allocate a robotic swarm around multiple boundaries in a way that is robust to environmental variations. Here, we build on this prior work to synthesize stochastic robot attachment–detachment policies for tasks in which a robotic swarm must achieve non-uniform spatial distributions around multiple loads and transport them at a constant velocity. Three methods are presented for designing robot control policies that replicate the steady-state distributions, transient dynamics, and fluxes between states that we have observed in ant populations during group retrieval. The equilibrium population matching method can be used to achieve a desired transport team composition as quickly as possible; the transient matching method can control the transient population dynamics of the team while driving it to the desired composition; and the rate matching method regulates the rates at which robots join and leave a load during transport. We validate our model predictions in an agent-based simulation, verify that each controller design method produces successful transport of a load at a regulated velocity, and compare the advantages and disadvantages of each method.     

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Search and rescue, autonomous construction, and many other semi-autonomous multirobot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator’s SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85 % on some subjects, while the average over the ten subjects was 75 %.     

Self-organized discrimination of resources

When selecting a resource to exploit, an insect colony must take into account at least two constraints: the resource must be abundant enough to sustain the whole group, but not too large to limit exploitation costs, and risks of conflicts with other colonies. Following recent results on cockroaches and ants, we introduce here a behavioral mechanism that satisfies these two constraints. Individuals simply modulate their probability to switch to another resource as a function of the local density of conspecifics locally detected. As a result, the individuals gather at the smallest resource that can host the whole group, hence reducing competition and exploitation costs while fulfilling the overall group's needs. Our analysis reveals that the group becomes better at discriminating between similar resources as it grows in size. Also, the discrimination mechanism is flexible and the group readily switches to a better suited resource as it appears in the environment. The collective decision emerges through the self-organization of individuals, that is, in absence of any centralized control. It also requires a minimal individual cognitive investment, making the proposed mechanism likely to occur in other social species and suitable for the development of distributed decision making tools.     

Collective behaviors of two-component swarms

We present a particle-based simulation study on two-component swarms where there exist two different types of groups in a swarm. Effects of different parameters between the two groups are studied systematically based on Langevin's equation. It is shown that the mass difference can introduce a protective behavior for the lighter members of the swarm in a vortex state. When the self-propelling strength is allowed to differ between two groups, it is observed that the swarm becomes spatially segregated and finally separated into two components at a certain critical value. We also investigate effects of different preferences for shelters on their collective decision making. In particular, it is found that the probability of selecting a shelter from the other varies sigmoidally as a function of the number ratio. The model is shown to describe the dynamics of the shelter choosing process of the cockroach–robot mixed group satisfactorily. It raises the possibility that the present model can be applied to the problems of pest control and fishing using robots and decoys.     

Self-organized podosomes are dynamic mechanosensors

Podosomes are self-organized, dynamic, actin-containing structures that adhere to the extracellular matrix via integrins [1-5]. Yet, it is not clear what regulates podosome dynamics and whether podosomes can function as direct mechanosensors, like focal adhesions [6-9]. We show here that myosin-II proteins form circular structures outside and at the podosome actin ring to regulate podosome dynamics. Inhibiting myosin-II-dependent tension dissipated podosome actin rings before dissipating the myosin-ring structure. As podosome rings changed size or shape, tractions underneath the podosomes were exerted onto the substrate and were abolished when myosin-light-chain activity was inhibited. The magnitudes of tractions were comparable to those generated underneath focal adhesions, and they increased with substrate stiffness. The dynamics of podosomes and of focal adhesions were different. Torsional tractions underneath the podosome rings were generated with rotations of podosome rings in a nonmotile, nonrotating cell, suggesting a unique feature of these circular structures. Stresses applied via integrins at the apical surface directly displaced podosomes near the basal surface. Stress-induced podosome displacements increased nonlinearly with applied stresses. Our results suggest that podosomes are dynamic mechanosensors in which interactions of myosin tension and actin dynamics are crucial for regulating these self-organized structures in living cells.     

Self-organized patterns of airway narrowing

The behavior of respiratory diseases such as asthma and COPD may involve complicated interactions among multiple factors. Theoretical and experimental data suggest that interdependence among the airways of the bronchial tree leads to the emergence of self-organized patterns of airway narrowing, ventilation defects, and other phenomena when a tipping point is passed. Additionally, evidence from several studies shows that the behavior of an isolated airway is different from an identical airway embedded in the bronchial tree so that experimental results of isolated elements such as airways, airway smooth muscle, or inflammatory pathways may not explain the whole organ behavior. However, there may be factors in the isolated elements that can dramatically change the complex system's behavior. More effective strategies for prevention or recovery from a disease, such as asthma, will depend on our progress in identifying and understanding the essential parts of the self-organized behavior that is involved.     

Self-organized instability in complex ecosystems

Why are some ecosystems so rich, yet contain so many rare species? High species diversity, together with rarity, is a general trend in neotropical forests and coral reefs. However, the origin of such diversity and the consequences of food web complexity in both species abundances and temporal fluctuations are not well understood. Several regularities are observed in complex, multispecies ecosystems that suggest that these ecologies might be organized close to points of instability. We explore, in greater depth, a recent stochastic model of population dynamics that is shown to reproduce: (i) the scaling law linking species number and connectivity; (ii) the observed distributions of species abundance reported from field studies (showing long tails and thus a predominance of rare species); (iii) the complex fluctuations displayed by natural communities (including chaotic dynamics); and (iv) the species-area relations displayed by rainforest plots. It is conjectured that the conflict between the natural tendency towards higher diversity due to immigration, and the ecosystem level constraints derived from an increasing number of links, leaves the system poised at a critical boundary separating stable from unstable communities, where large fluctuations are expected to occur. We suggest that the patterns displayed by species-rich communities, including rarity, would result from such a spontaneous tendency towards instability.     

Selectivity in metabolic cooperation between cultured mammalian cells

Selective communication between cultured mammalian cells was detected as selectivity in metabolic cooperation. Whilst the majority of the cell types examined (human skin fibroblast, PC13, G3, Don, PyY) showed metabolic cooperation at almost all (>95%) of their homotypic cell-to-cell contacts, they did not necessarily show cooperation at such a high proportion of their heterotypic contacts. Less than 10% of G3/human fibroblast contacts, and usually less than 30% of G3/PC13 contacts were observed to be positive for metabolic cooperation. L cells differed from these other cell types in that they formed permeable junctions at a greater proportion of their heterotypic cell-to-cell contacts (contacts between L and PyY cells) than their homotypic contacts. We question why it was that the contacts between any two poorly-compatible cell types were positive for metabolic cooperation in only a small proportion of cases. We could find no indication that this phenomenon was attributable to heterogeneity within the cell stocks. Time course studies upon PC13 and G3 cells showed that the proportion of cooperation-positive contacts was not constant but that it continued to increase over many hours of co-culture. In comparing the homotypic and heterotypic interactions of these cell types, selectivity manifested as a difference in the rate of appearance of permeable junctions. We discuss possible explanations for these findings.     

Functional cooperation between the microtubule and actin cytoskeletons

In diverse cell types, microtubule (MT) and actin filament networks cooperate functionally during a wide variety of processes, including vesicle and organelle transport, cleavage furrow placement, directed cell migration, spindle rotation, and nuclear migration. The mechanisms by which MTs and actin filaments cooperate to mediate these different processes can be grouped into two broad categories: coordinated MT- and actin-based transport to move vesicles, organelles, and cell fate determinants; and targeting and capture of MT ends at cortical actin sites. Over the past several years, a growing number of cellular factors that bridge these cytoskeletal systems have been identified. These include 'hetero-motor' complexes (physically associated myosin and kinesin), myosin-CLIP170 complexes, formin homology (FH) proteins, dynein and the dynactin complex, Kar9p, coronin, Kelch repeat-containing proteins, and ERM proteins.     

Self-organized criticality in ant brood tending

A new stochastic lattice gas model of ant brood tending is formulated to examine the role played by repulsive ant-ant interactions in the even distribution of care among brood members. The deterministic limit of the model is known to be self-organized critical. Numerical simulations of the model show that the ant-ant repulsion facilitates an even distribution of brood care in the middle of the brood. This provides a possible explanation for the fact that ants sort their brood so that the youngest brood (which are most in need of care) are placed in the middle. Simulations show that the uniformity of brood care distribution is optimal when ants operate in a regime intermediate between completely random and completely deterministic. A certain degree of randomness helps ants to avoid becoming trapped in suboptimal configurations but does not destroy the long-range correlations that are inherent to self-organized critical systems.     

Quantifying and tracing information cascades in swarms

We propose a novel, information-theoretic, characterisation of cascades within the spatiotemporal dynamics of swarms, explicitly measuring the extent of collective communications. This is complemented by dynamic tracing of collective memory, as another element of distributed computation, which represents capacity for swarm coherence. The approach deals with both global and local information dynamics, ultimately discovering diverse ways in which an individual's spatial position is related to its information processing role. It also allows us to contrast cascades that propagate conflicting information with waves of coordinated motion. Most importantly, our simulation experiments provide the first direct information-theoretic evidence (verified in a simulation setting) for the long-held conjecture that the information cascades occur in waves rippling through the swarm. Our experiments also exemplify how features of swarm dynamics, such as cascades' wavefronts, can be filtered and predicted. We observed that maximal information transfer tends to follow the stage with maximal collective memory, and principles like this may be generalised in wider biological and social contexts.     

Self-organized dynamics in spatially structured populations

Self-organization and pattern formation represent the emergence of order in temporal and spatial processes. Self-organization in population ecology is gaining attention due to the recent advances concerning temporal fluctuations in the population size of dispersal-linked subunits. We shall report that spatially structured models of population renewal promote the emergence of a complex power law order in spatial population dynamics. We analyse a variety of population models showing that self-organization can be identified as a temporal match in population dynamics among local units, and how the synchrony changes in time. Our theoretical results are concordant with analyses of population data on the Canada lynx.