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.     

Self-organised path formation in a swarm of robots

In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.     

Interactive robots in experimental biology

Interactive robots have the potential to revolutionise the study of social behaviour because they provide several methodological advances. In interactions with live animals, the behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that different morphologies and behavioural strategies can be combined), behaviour can be manipulated in complex interaction sequences and models of behaviour can be embodied by the robot and thereby be tested. Furthermore, robots can be used as demonstrators in experiments on social learning. As we discuss here, the opportunities that robots create for new experimental approaches have far-reaching consequences for research in fields such as mate choice, cooperation, social learning, personality studies and collective behaviour.     

Energy-efficient indoor search by swarms of simulated flying robots without global information

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints.     

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.     

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.     

Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making

Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system??s behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model??s predictions through a swarm robotics experiment in which robot teams must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.     

Environmental variability in the early rearing environment generates behaviourally flexible cod: implications for rehabilitating wild populations

The release of hatchery-reared fishes for restoring threatened and endangered populations is one of the most controversial issues in applied ecology. A central issue has been to determine whether releases cause extinction of local wild populations. This may arise either through domesticated or non-local fishes hybridizing with wild fishes, or through inappropriate behavioural interactions; for example, many hatchery fishes show exaggerated aggressive and competitive behaviour and out-compete wild counterparts. The impact of the impoverished hatchery environment in shaping behaviour is only now receiving attention. Attempts to counteract hatchery-related behavioural deficiencies have utilized intensive training programmes shortly before the fishes are released. However, we show here that simple exposure to variable spatial and foraging cues in the standard hatchery environment generates fishes with enhanced behavioural traits that are probably associated with improved survival in the wild. It appears that fishes need to experience a varying and changeable environment to learn and develop flexible behaviour. Using variable hatchery rearing environments to generate suitable phenotypes in combination with a knowledge of appropriate local genotypes, rehabilitation of wild fishes is likely to succeed, where to date it has largely failed.     

Competition avoidance drives individual differences in response to a changing food resource in sticklebacks

Within the same population, individuals often differ in how they respond to changes in their environment. A recent series of models predicts that competition in a heterogeneous environment might promote between‐individual variation in behavioural plasticity. We tested groups of sticklebacks in patchy foraging environments that differed in the level of competition. We also tested the same individuals across two different social groups and while alone to determine the social environment's influence on behavioural plasticity. In support of model predictions, individuals consistently differed in behavioural plasticity when the presence of conspecifics influenced the potential payoffs of a foraging opportunity. Whether individuals maintained their level of behavioural plasticity when placed in a new social group depended on the environmental heterogeneity. By explicitly testing predictions of recent theoretical models, we provide evidence for the types of ecological conditions under which we would expect, and not expect, variation in behavioural plasticity to be favoured.     

From network-to-antibody robustness in a bio-inspired immune system

Behavioural robustness at antibody and immune network level is discussed. The robustness of the immune response that drives an autonomous mobile robot is examined with two computational experiments in the autonomous mobile robots trajectory generation context in unknown environments. The immune response is met based on the immune network metaphor for different low-level behaviours coordination. These behaviours are activated when a robot sense the appropriate conditions in the environment in relation to the network current state. Results are obtained over a case study in computer simulation as well as in laboratory experiments with a Khepera II microrobot. In this work, we develop a set of tests where such an immune response is externally perturbed at network or low-level behavioural modules to analyse the robust capacity of the system to unexpected perturbations. Emergence of robust behaviour and high-level immune response relates to the coupling between behavioural modules that are selectively engaged with the environment based on immune response. Experimental evidence leads discussions on a dynamical systems perspective of behavioural robustness in artificial immune systems that goes beyond the isolated immune network response.     

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.     

Individual differences in plasticity and sampling when playing behavioural games

When engaged in behavioural games, animals can adjust their use of alternative tactics until groups reach stable equilibria. Recent theory on behavioural plasticity in games predicts that individuals should differ in their plasticity or responsiveness and hence in their degree of behavioural adjustment. Moreover, individuals are predicted to be consistent in their plasticity within and across biological contexts. These predictions have yet to be tested empirically and so we examine the behavioural adjustment of individual nutmeg mannikins (Lonchura punctulata), gregarious ground-feeding passerines, when playing two different social foraging games: producer-scrounger (PS) and patch-choice (PC) games. We found: (i) significant individual differences in plasticity and sampling behaviour in each of the two games, (ii) individual differences in sampling behaviour were consistent over different test conditions within a game (PC) and over a six month period (PS), (iii) but neither individual plasticity nor sampling behaviour was correlated from one social foraging game to another. The rate at which birds sampled alternative tactics was positively associated with seed intake in PS trials but negatively associated in PC trials. These results suggest that games with frequency dependence of pay-offs can maintain differences in behavioural plasticity but that an important component of this plasticity is group- and/or context-specific.     

Brain size predicts behavioural plasticity in guppies (Poecilia reticulata): An experiment

Understanding how animal personality (consistent between‐individual behavioural differences) arises has become a central topic in behavioural sciences. This endeavour is complicated by the fact that not only the mean behaviour of individuals (behavioural type) but also the strength of their reaction to environmental change (behavioural plasticity) varies consistently. Personality and cognitive abilities are linked, and we suggest that behavioural plasticity could also be explained by differences in brain size (a proxy for cognitive abilities), since accurate decisions are likely essential to make behavioural plasticity beneficial. We test this idea in guppies (Poecilia reticulata), artificially selected for large and small brain size, which show clear cognitive differences between selection lines. To test whether those lines differed in behavioural plasticity, we reared them in groups in structurally enriched environments and then placed adults individually into empty tanks, where we presented them daily with visual predator cues and monitored their behaviour for 20 days with video‐aided motion tracking. We found that individuals differed consistently in activity and risk‐taking, as well as in behavioural plasticity. In activity, only the large‐brained lines demonstrated habituation (increased activity) to the new environment, whereas in risk‐taking, we found sensitization (decreased risk‐taking) in both brain size lines. We conclude that brain size, potentially via increasing cognitive abilities, may increase behavioural plasticity, which in turn can improve habituation to novel environments. However, the effects seem to be behaviour‐specific. Our results suggest that brain size likely explains some of the variation in behavioural plasticity found at the intraspecific level.     

Evolving coordinated group behaviours through maximisation of mean mutual information

A well known problem in the design of the control system for a swarm of robots concerns the definition of suitable individual rules that result in the desired coordinated behaviour. A possible solution to this problem is given by the automatic synthesis of the individual controllers through evolutionary or learning processes. These processes offer the possibility to freely search the space of the possible solutions for a given task, under the guidance of a user-defined utility function. Nonetheless, there exist no general principles to follow in the definition of such a utility function in order to reward coordinated group behaviours. As a consequence, task dependent functions must be devised each time a new coordination problem is under study. In this paper, we propose the use of measures developed in Information Theory as task-independent, implicit utility functions. We present two experiments in which three robots are trained to produce generic coordinated behaviours. Each robot is provided with rich sensory and motor apparatus, which can be exploited to explore the environment and to communicate with other robots. We show how coordinated behaviours can be synthesised through a simple evolutionary process. The only criteria used to evaluate the performance of the robotic group is the estimate of mutual information between the motor states of the robots.     

Emotion Attribution to a Non-Humanoid Robot in Different Social Situations

In the last few years there was an increasing interest in building companion robots that interact in a socially acceptable way with humans. In order to interact in a meaningful way a robot has to convey intentionality and emotions of some sort in order to increase believability. We suggest that human-robot interaction should be considered as a specific form of inter-specific interaction and that human–animal interaction can provide a useful biological model for designing social robots. Dogs can provide a promising biological model since during the domestication process dogs were able to adapt to the human environment and to participate in complex social interactions. In this observational study we propose to design emotionally expressive behaviour of robots using the behaviour of dogs as inspiration and to test these dog-inspired robots with humans in inter-specific context. In two experiments (wizard-of-oz scenarios) we examined humans'' ability to recognize two basic and a secondary emotion expressed by a robot. In Experiment 1 we provided our companion robot with two kinds of emotional behaviour (“happiness” and “fear”), and studied whether people attribute the appropriate emotion to the robot, and interact with it accordingly. In Experiment 2 we investigated whether participants tend to attribute guilty behaviour to a robot in a relevant context by examining whether relying on the robot''s greeting behaviour human participants can detect if the robot transgressed a predetermined rule. Results of Experiment 1 showed that people readily attribute emotions to a social robot and interact with it in accordance with the expressed emotional behaviour. Results of Experiment 2 showed that people are able to recognize if the robot transgressed on the basis of its greeting behaviour. In summary, our findings showed that dog-inspired behaviour is a suitable medium for making people attribute emotional states to a non-humanoid robot.     

Geographic and seasonal variability in feeding behaviour of a small herbivorous rodent

Foraging strategies have traditionally been modelled as a result of food selection in response to one factor, as for instance resource availability, deterrent compounds or nutrients. Thus, a trade-off is assumed between plasticity (generalist strategy) and efficiency (specialist strategy). Nevertheless, several studies have demonstrated that animals cope behaviourally with food supply variation. For instance, desert-dwelling rodents partially compensate for nutritional bottlenecks through diet selection. The aim of our study was to test how foraging behaviour matches spatial and temporal variations in the trophic environment and how modelling hypotheses help us to understand the resultant foraging strategy. Our animal study model was the small cavy Microcavia australis, a widely distributed herbivorous rodent. Fieldwork was carried out in four places, in wet and dry seasons. We found significant differences in plant cover, plant diversity and niche breadth, and diet selection revealed a complex foraging strategy. M. australis shows a behavioural repertoire that exceeds single-criterion categories; therefore, we appeal to theoretical models that consider ecological and physiological perspectives. We classified the small cavy as a facultative specialist displaying a thoroughly opportunistic strategy based on the plasticity of the behavioural phenotype. We finally discuss the evolutionary relevance of our results and propose further investigation avenues.     

Artificial pheromone for path selection by a foraging swarm of robots

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.     

Task-specific expression of the foraging gene in harvester ants

In social insects, groups of workers perform various tasks such as brood care and foraging. Transitions in workers from one task to another are important in the organization and ecological success of colonies. Regulation of genetic pathways can lead to plasticity in social insect task behaviour. The colony organization of advanced eusocial insects evolved independently in ants, bees, and wasps and it is not known whether the genetic mechanisms that influence behavioural plasticity are conserved across species. Here we show that a gene associated with foraging behaviour is conserved across social insect species, but the expression patterns of this gene are not. We cloned the red harvester ant (Pogonomyrmex barbatus) ortholog (Pbfor) to foraging, one of few genes implicated in social organization, and found that foraging behaviour in harvester ants is associated with the expression of this gene; young (callow) worker brains have significantly higher levels of Pbfor mRNA than foragers. Levels of Pbfor mRNA in other worker task groups vary among harvester ant colonies. However, foragers always have the lowest expression levels compared to other task groups. The association between foraging behaviour and the foraging gene is conserved across social insects but ants and bees have an inverse relationship between foraging expression and behaviour.     

Behavioural comparison of human-animal (dog) and human-robot (AIBO) interactions

The behavioural analysis of human-robot interactions can help in developing socially interactive robots. The current study analyzes human-robot interaction with Theme software and the corresponding pattern detection algorithm. The method is based on the analysis of the temporal structure of the interactions by detecting T-patterns in the behaviour. We have compared humans' (children and adults) play behaviour interacting either with an AIBO or a living dog puppy. The analysis based on measuring latencies and frequencies of behavioural units suggested limited differences, e.g. the latency of humans touching the dog/AIBO was similar. In addition other differences could be accounted for by the limited abilities of the robot to interact with objects. Although the number of interactive T-patterns did not significantly differ among the groups but the partner's type (whether humans were playing with dog or AIBO) had a significant effect on the structure of the patterns. Both children and adults terminated T-patterns more frequently when playing with AIBO than when playing with the dog puppy, which suggest that the robot has a limited ability to engage in temporally structured behavioural interactions with humans. As other human studies suggest that the temporal complexity of the interaction is good measure of the partner's attitude, we suggest that more attention should be paid in the future to the robots' ability to engage in cooperative interaction with humans.