Artificial Pheromone System Using RFID for Navigation of Autonomous Robots

Navigation system based on the animal behavior has received a growing attention in the past few years. The navigation systems using artificial pheromone are still few so far. For this reason, this paper presents our research that aim to implement autonomous navigation with artificial pheromone system. By introducing artificial pheromone system composed of data carriers and autonomous robots, the robotic system creates a potential field to navigate their group. We have developed a pheromone density model to realize the function of pheromones with the help of data carriers. We intend to show the effectiveness of the proposed system by performing simulations and realization using modified mobile robot. The pheromone potential field system can be used for navigation of autonomous robots.     

Emergent Runaway into an Avoidance Area in a Swarm of Soldier Crabs

Emergent behavior that arises from a mass effect is one of the most striking aspects of collective animal groups. Investigating such behavior would be important in order to understand how individuals interact with their neighbors. Although there are many experiments that have used collective animals to investigate social learning or conflict between individuals and society such as that between a fish and a school, reports on mass effects are rare. In this study, we show that a swarm of soldier crabs could spontaneously enter a water pool, which are usually avoided, by forming densely populated part of a swarm at the edge of the water pool. Moreover, we show that the observed behavior can be explained by the model of collective behavior based on inherent noise that is individuals’ different velocities in a directed group. Our results suggest that inherent noise, which is widely seen in collective animals, can contribute to formation and/or maintenance of a swarm and that the dense swarm can enter the pool by means of enhanced inherent noise.     

Convergent evolution and locomotion through complex terrain by insects, vertebrates and robots

Arthropods are the most successful members of the animal kingdom largely because of their ability to move efficiently through a range of environments. Their agility has not been lost on engineers seeking to design agile legged robots. However, one cannot simply copy mechanical and neural control systems from insects into robotic designs. Rather one has to select the properties that are critical for specific behaviors that the engineer wants to capture in a particular robot. Convergent evolution provides an important clue to the properties of legged locomotion that are critical for success. Arthropods and vertebrates evolved legged locomotion independently. Nevertheless, many neural control properties and mechanical schemes are remarkably similar. Here we describe three aspects of legged locomotion that are found in both insects and vertebrates and that provide enhancements to legged robots. They are leg specialization, body flexion and the development of a complex head structure. Although these properties are commonly seen in legged animals, most robotic vehicles have similar legs throughout, rigid bodies and rudimentary sensors on what would be considered the head region. We describe these convergent properties in the context of robots that we developed to capture the agility of insects in moving through complex terrain.     

Collective animal behavior from Bayesian estimation and probability matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.     

The biological principles of swarm intelligence

The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects’ colonies. We begin with some reminders about the decentralized nature of such systems and we describe the underlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in particular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system’s parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we address the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that future studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds.     

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport.     

Imitation Combined with a Characteristic Stimulus Duration Results in Robust Collective Decision-Making

For group-living animals, reaching consensus to stay cohesive is crucial for their fitness, particularly when collective motion starts and stops. Understanding the decision-making at individual and collective levels upon sudden disturbances is central in the study of collective animal behavior, and concerns the broader question of how information is distributed and evaluated in groups. Despite the relevance of the problem, well-controlled experimental studies that quantify the collective response of groups facing disruptive events are lacking. Here we study the behavior of small-sized groups of uninformed individuals subject to the departure and stop of a trained conspecific. We find that the groups reach an effective consensus: either all uninformed individuals follow the trained one (and collective motion occurs) or none does. Combining experiments and a simple mathematical model we show that the observed phenomena results from the interplay between simple mimetic rules and the characteristic duration of the stimulus, here, the time during which the trained individual is moving away. The proposed mechanism strongly depends on group size, as observed in the experiments, and even if group splitting can occur, the most likely outcome is always a coherent collective group response (consensus). The prevalence of a consensus is expected even if the groups of naives face conflicting information, e.g. if groups contain two subgroups of trained individuals, one trained to stay and one trained to leave. Our results indicate that collective decision-making and consensus in (small) animal groups are likely to be self-organized phenomena that do not involve concertation or even communication among the group members.     

Parasitic Robot System for Waypoint Navigation of Turtle

In research on small mobile robots and biomimetic robots,locomotion ability remains a major issue despite many advances in technology.However,evolution has led to there being many real animals capable of excellent locomotion.This paper presents a "parasitic robot system" whereby locomotion abilities of an animal are applied to a robot task.We chose a turtle as our first host animal and designed a parasitic robot that can perform "operant conditioning".The parasitic robot,which is attached to the turtle,can induce object-tracking behavior of the turtle toward a Light Emitting Diode (LED) and positively reinforce the behavior through repeated stimulus-response interaction.After training sessions over five weeks,the robot could successfully control the direction of movement of the trained turtles in the waypoint navigation task.This hybrid animal-robot interaction system could provide an alternative solution to some of the limitations of conventional mobile robot systems in various fields,and could also act as a useful interaction system for the behavioral sciences.     

Designing pheromone communication in swarm robotics: Group foraging behavior mediated by chemical substance

In swarm robotics, communication among the robots is essential. Inspired by biological swarms using pheromones, we propose the use of chemical compounds to realize group foraging behavior in robot swarms. We designed a fully autonomous robot, and then created a swarm using ethanol as the trail pheromone allowing the robots to communicate with one another indirectly via pheromone trails. Our group recruitment and cooperative transport algorithms provide the robots with the required swarm behavior. We conducted both simulations and experiments with real robot swarms, and analyzed the data statistically to investigate any changes caused by pheromone communication in the performance of the swarm in solving foraging recruitment and cooperative transport tasks. The results show that the robots can communicate using pheromone trails, and that the improvement due to pheromone communication may be non-linear, depending on the size of the robot swarm.     

Collective Response of Zebrafish Shoals to a Free-Swimming Robotic Fish

In this work, we explore the feasibility of regulating the collective behavior of zebrafish with a free-swimming robotic fish. The visual cues elicited by the robot are inspired by salient features of attraction in zebrafish and include enhanced coloration, aspect ratio of a fertile female, and carangiform/subcarangiform locomotion. The robot is autonomously controlled with an online multi-target tracking system and swims in circular trajectories in the presence of groups of zebrafish. We investigate the collective response of zebrafish to changes in robot speed, achieved by varying its tail-beat frequency. Our results show that the speed of the robot is a determinant of group cohesion, quantified through zebrafish nearest-neighbor distance, which increases with the speed of the robot until it reaches . We also find that the presence of the robot causes a significant decrease in the group speed, which is not accompanied by an increase in the freezing response of the subjects. Findings of this study are expected to inform the design of experimental protocols that leverage the use of robots to study the zebrafish animal model.     

Collective Movement in the Tibetan Macaques (Macaca thibetana): Early Joiners Write the Rule of the Game

Collective behavior has recently attracted a great deal of interest in both natural and social sciences. While the role of leadership has been closely scrutinized, the rules used by joiners in collective decision making have received far less attention. Two main hypotheses have been proposed concerning these rules: mimetism and quorum. Mimetism predicts that individuals are increasingly likely to join collective behavior as the number of participants increases. It can be further divided into selective mimetism, where relationships among the participants affect the process, and anonymous mimetism, where no such effect exists. Quorum predicts that a collective behavior occurs when the number of participants reaches a threshold. To probe into which rule is used in collective decision making, we conducted a study on the joining process in a group of free-ranging Tibetan macaques (Macaca thibetana) in Huangshan, China using a combination of all-occurrence and focal animal sampling methods. Our results show that the earlier individuals joined movements, the more central a role they occupied among the joining network. We also found that when less than three adults participated in the first five minutes of the joining process, no entire group movement occurred subsequently. When the number of these early joiners ranged from three to six, selective mimetism was used. This means higher rank or closer social affiliation of early joiners could be among the factors of deciding whether to participate in movements by group members. When the number of early joiners reached or exceeded seven, which was the simple majority of the group studied, entire group movement always occurred, meaning that the quorum rule was used. Putting together, Macaca thibetana used a combination of selective mimetism and quorum, and early joiners played a key role in deciding which rule should be used.     

The principles of collective animal behaviour

In recent years, the concept of self-organization has been used to understand collective behaviour of animals. The central tenet of self-organization is that simple repeated interactions between individuals can produce complex adaptive patterns at the level of the group. Inspiration comes from patterns seen in physical systems, such as spiralling chemical waves, which arise without complexity at the level of the individual units of which the system is composed. The suggestion is that biological structures such as termite mounds, ant trail networks and even human crowds can be explained in terms of repeated interactions between the animals and their environment, without invoking individual complexity. Here, I review cases in which the self-organization approach has been successful in explaining collective behaviour of animal groups and societies. Ant pheromone trail networks, aggregation of cockroaches, the applause of opera audiences and the migration of fish schools have all been accurately described in terms of individuals following simple sets of rules. Unlike the simple units composing physical systems, however, animals are themselves complex entities, and other examples of collective behaviour, such as honey bee foraging with its myriad of dance signals and behavioural cues, cannot be fully understood in terms of simple individuals alone. I argue that the key to understanding collective behaviour lies in identifying the principles of the behavioural algorithms followed by individual animals and of how information flows between the animals. These principles, such as positive feedback, response thresholds and individual integrity, are repeatedly observed in very different animal societies. The future of collective behaviour research lies in classifying these principles, establishing the properties they produce at a group level and asking why they have evolved in so many different and distinct natural systems. Ultimately, this research could inform not only our understanding of animal societies, but also the principles by which we organize our own society.     

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.     

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.     

Connecting the dots in ethology: applying network theory to understand neural and animal collectives

A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach, thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure–function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales.     

Communication and Cognition in Primate Group Movement

We here review the communicative and cognitive processes underpinning collective group movement in animals. Generally, we identify 2 major axes to explain the dynamics of decision making in animal or human groups or aggregations: One describes whether the behavior is largely determined by simple rules such as keeping a specific distance from the neighbor, or whether global information is also factored in. The second axis describes whether or not the individual constituents of the group have overlapping or diverging interests. We then review the available evidence for baboons, which have been particularly well studied, but we also draw from further studies on other nonhuman primate species. Baboons and other nonhuman primates may produce specific signals in the group movement context, such as the notifying behavior of male hamadryas baboons at the departure from the sleeping site, or clear barks that are given by chacma baboons that have lost contact with the group or specific individuals. Such signals can be understood as expressions of specific motivational states of the individuals, but there is no evidence that the subjects intend to alter the knowledge state of the recipients. There is also no evidence for shared intentionality. The cognitive demands that are associated with decision making in the context of group coordination vary with the amount of information and possibly conflicting sources of information that need to be integrated. Thus, selective pressures should favor the use of signals that maintain group cohesion, while recipients should be selected to be able to make the decision that is in their own best interest in light of all the available information.     

Decisions,decisions, decisions: choosing a biological science of choice

Behavioral ecologists argue that evolution drives animal behavior to efficiently solve the problems animals face in their environmental niches. The ultimate evolutionary causes of decision making, they contend, can be found in economic analyses of organisms and their environments. Neurobiologists interested in how animals make decisions have, in contrast, focused their efforts on understanding the neurobiological hardware that serves as a more proximal cause of that same behavior. Describing the flow of information within the nervous system without regard to these larger goals has been their focus. Recent work in a number of laboratories has begun to suggest that these two approaches are beginning to fuse. It may soon be possible to view the nervous system as a representational process that solves the mathematically defined economic problems animals face by making efficient decisions. These developments in the neurobiological theory of choice, and the new schema they imply, form the subject of this article.     

From the first intention movement to the last joiner: macaques combine mimetic rules to optimize their collective decisions

Mechanisms related to collective decision making have recently been found in almost all animal reigns from amoebae to worms, insects and vertebrates, including human beings. Decision-making mechanisms related to collective movements-including pre-departure and joining-have already been studied at different steps of the movement process, but these studies were always carried out separately. We therefore have no understanding of how these different processes are related when they underlie the same collective decision-making event. Here, we consider the whole departure process of two groups of Tonkean macaques (Macaca tonkeana), using a stochastic model. When several exclusive choices are proposed, macaques vote and choose the majority. Individuals then join the movement according to a mimetism based on affiliative relationships. The pre-departure quorum and the joining mimetic mechanism are probably linked, but we have not yet identified which transition mechanism is used. This study shows that decision-making related to macaque group movements is governed by a quorum rule combined with a selective mimetism at departure. This is the first time that transition mechanisms have been described in mammals, which consequently helps understand how a voting process leads to social amplification. Our study also provides the first complete proof that there is continuity in the decision-making processes underlying collective movements in mammals from the first intention movement right through to the last joiner.     

To follow or not? How animals in fusion–fission societies handle conflicting information during group decision‐making

When group members possess differing information about the environment, they may disagree on the best movement decision. Such conflicts result in group break‐ups, and are therefore a fundamental driver of fusion–fission group dynamics. Yet, a paucity of empirical work hampers our understanding of how adaptive evolution has shaped plasticity in collective behaviours that promote and maintain fusion–fission dynamics. Using movement data from GPS‐collared bison, we found that individuals constantly associated with other animals possessing different spatial knowledge, and both personal and conspecific information influenced an individual's patch choice decisions. During conflict situations, bison used group familiarity coupled with their knowledge of local foraging options and recently sampled resource quality when deciding to follow or leave a group – a tactic that led to energy‐rewarding movements. Natural selection has shaped collective behaviours for coping with social conflicts and resource heterogeneity, which maintain fusion–fission dynamics and play an essential role in animal distribution.