Self-organisation and communication in groups of simulated and physical robots

In social insects, both self-organisation and communication play a crucial role for the accomplishment of many tasks at a collective level. Communication is performed with different modalities, which can be roughly classified into three classes: indirect (stigmergic) communication, direct interactions and direct communication. The use of stigmergic communication is predominant in social insects (e.g. the pheromone trails in ants), where, however, direct interactions (e.g. antennation in ants) and direct communication (e.g. the waggle dance in honey bees) can also be observed. Taking inspiration from insect societies, we present an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities. In particular, the robots are placed in an arena presenting holes and open borders, which they should avoid while moving coordinately. Artificial evolution is responsible for the synthesis in a simulated environment of the robot’s neural controllers, which are subsequently tested on physical robots. We study different communication strategies among the robots: no direct communication, handcrafted signalling and a completely evolved approach. We show that the latter is the most efficient, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies. Moreover, we show that the evolved controllers produce a self-organising system that is robust enough to be tested on physical robots, notwithstanding the huge gap between simulation and reality.Electronic Supplementary Material Supplementary material is available for this article at     

Active Collisions in Altered Gravity Reveal Eye-Hand Coordination Strategies

Most object manipulation tasks involve a series of actions demarcated by mechanical contact events, and gaze is usually directed to the locations of these events as the task unfolds. Typically, gaze foveates the target 200 ms in advance of the contact. This strategy improves manual accuracy through visual feedback and the use of gaze-related signals to guide the hand/object. Many studies have investigated eye-hand coordination in experimental and natural tasks; most of them highlighted a strong link between eye movements and hand or object kinematics. In this experiment, we analyzed gaze strategies in a collision task but in a very challenging dynamical context. Participants performed collisions while they were exposed to alternating episodes of microgravity, hypergravity and normal gravity. First, by isolating the effects of inertia in microgravity, we found that peak hand acceleration marked the transition between two modes of grip force control. Participants exerted grip forces that paralleled load force profiles, and then increased grip up to a maximum shifted after the collision. Second, we found that the oculomotor strategy adapted visual feedback of the controlled object around the collision, as demonstrated by longer durations of fixation after collision in new gravitational environments. Finally, despite large variability of arm dynamics in altered gravity, we found that saccades were remarkably time-locked to the peak hand acceleration in all conditions. In conclusion, altered gravity allowed light to be shed on predictive mechanisms used by the central nervous system to coordinate gaze, hand and grip motor actions during a mixed task that involved transport of an object and high impact loads.     

Evolutionary stability of vigilance coordination among social foragers

Coordination can greatly improve the efficiency of anti-predatory vigilance scans by increasing predator detection for a constant proportion of time spent vigilant. However, it has been rarely found in nature and most studies have detected or assumed independent scanning by group members. In this study, we analysed the functional consequences of the coordinated alternation of vigilance scanning by group foragers. We introduce coordination by assuming that interscan intervals (ISIs) follow a modified gamma distribution. Depending on the parameters of the distribution, successive scans can be evenly spaced (coordinated scanning) or may present a high overlap (uncoordinated scanning). Comparing evolutionarily stable strategies for animals that do not coordinate their scanning with animals that do coordinate their anti-predator behaviour shows that coordination has a marked effect on survival probability. Moreover, the coordinating strategy is quite robust against mutants that scan independently with exponential distributions of ISIs. However, coordination breaks down when animals can continuously adjust their level of coordination by deciding the proportion of time they spend monitoring the behaviour of other group members. In this case, coordination is only evolutionarily stable if it can be very easily achieved.     

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.     

Light intensity-dependent retrograde signalling in higher plants

Plants are able to acclimate to highly fluctuating light environment and evolved a short- and long-term light acclimatory responses, that are dependent on chloroplasts retrograde signalling. In this review we summarise recent evidences suggesting that the chloroplasts act as key sensors of light intensity changes in a wide range (low, high and excess light conditions) as well as sensors of darkness. They also participate in transduction and synchronisation of systemic retrograde signalling in response to differential light exposure of distinct leaves. Regulation of intra- and inter-cellular chloroplast retrograde signalling is dependent on the developmental and functional stage of the plastids. Therefore, it is discussed in following subsections: firstly, chloroplast biogenic control of nuclear genes, for example, signals related to photosystems and pigment biogenesis during early plastid development; secondly, signals in the mature chloroplast induced by changes in photosynthetic electron transport, reactive oxygen species, hormones and metabolite biosynthesis; thirdly, chloroplast signalling during leaf senescence. Moreover, with a help of meta-analysis of multiple microarray experiments, we showed that the expression of the same set of genes is regulated specifically in particular types of signals and types of light conditions. Furthermore, we also highlight the alternative scenarios of the chloroplast retrograde signals transduction and coordination linked to the role of photo-electrochemical signalling.     

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.     

Cache consensus: rapid object sorting by a robotic swarm

We present a new method which allows a swarm of robots to sort arbitrarily arranged objects into homogeneous clusters. In the ideal case, a distributed robotic sorting method should establish a single homogeneous cluster for each object type. This can be achieved with existing methods, but the rate of convergence is considered too slow for real-world application. Previous research on distributed robotic sorting is typified by randomised movement with a pick-up/deposit behaviour that is a probabilistic function of local object density. We investigate whether the ability of each robot to localise and return to remembered places can improve distributed sorting performance. In our method, each robot maintains a cache point for each object type. Upon collecting an object, it returns to add this object to the cluster surrounding the cache point. Similar to previous biologically inspired work on distributed sorting, no explicit communication between robots is implemented. However, the robots can still come to a consensus on the best cache for each object type by observing clusters and comparing their sizes with remembered cache sizes. We refer to this method as cache consensus. Our results indicate that incorporating this localisation capability enables a significant improvement in the rate of convergence. We present experimental results using a realistic simulation of our targeted robotic platform. A subset of these experiments is also validated on physical robots.     

In Good Company? Perception of Movement Synchrony of a Non-Anthropomorphic Robot

Recent technological developments like cheap sensors and the decreasing costs of computational power have brought the possibility of robotic home companions within reach. In order to be accepted it is vital for these robots to be able to participate meaningfully in social interactions with their users and to make them feel comfortable during these interactions. In this study we investigated how people respond to a situation where a companion robot is watching its user. Specifically, we tested the effect of robotic behaviours that are synchronised with the actions of a human. We evaluated the effects of these behaviours on the robot’s likeability and perceived intelligence using an online video survey. The robot used was Care-O-bot3, a non-anthropomorphic robot with a limited range of expressive motions. We found that even minimal, positively synchronised movements during an object-oriented task were interpreted by participants as engagement and created a positive disposition towards the robot. However, even negatively synchronised movements of the robot led to more positive perceptions of the robot, as compared to a robot that does not move at all. The results emphasise a) the powerful role that robot movements in general can have on participants’ perception of the robot, and b) that synchronisation of body movements can be a powerful means to enhance the positive attitude towards a non-anthropomorphic robot.     

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.     

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.     

Biomimetic vibrissal sensing for robots

Active vibrissal touch can be used to replace or to supplement sensory systems such as computer vision and, therefore, improve the sensory capacity of mobile robots. This paper describes how arrays of whisker-like touch sensors have been incorporated onto mobile robot platforms taking inspiration from biology for their morphology and control. There were two motivations for this work: first, to build a physical platform on which to model, and therefore test, recent neuroethological hypotheses about vibrissal touch; second, to exploit the control strategies and morphology observed in the biological analogue to maximize the quality and quantity of tactile sensory information derived from the artificial whisker array. We describe the design of a new whiskered robot, Shrewbot, endowed with a biomimetic array of individually controlled whiskers and a neuroethologically inspired whisking pattern generation mechanism. We then present results showing how the morphology of the whisker array shapes the sensory surface surrounding the robot's head, and demonstrate the impact of active touch control on the sensory information that can be acquired by the robot. We show that adopting bio-inspired, low latency motor control of the rhythmic motion of the whiskers in response to contact-induced stimuli usefully constrains the sensory range, while also maximizing the number of whisker contacts. The robot experiments also demonstrate that the sensory consequences of active touch control can be usefully investigated in biomimetic robots.     

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.     

Bio-inspired design strategies for central pattern generator control in modular robotics

New findings in the nervous system of invertebrates have shown how a number of features of central pattern generator (CPG) circuits contribute to the generation of robust flexible rhythms. In this paper we consider recently revealed strategies that living CPGs follow to design CPG control paradigms for modular robots. To illustrate them, we divide the task of designing an example CPG for a modular robot into independent problems. We formulate each problem in a general way and provide a bio-inspired solution for each of them: locomotion information coding, individual module control and inter-module coordination. We analyse the stability of the CPG numerically, and then test it on a real robot. We analyse steady state locomotion and recovery after perturbations. In both cases, the robot is able to autonomously find a stable effective locomotion state. Finally, we discuss how these strategies can result in a more general design approach for CPG-based locomotion.     

A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was applied for three different ocean datasets: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system – across the spatial (between individual sensors) and temporal dimensions of the sensor data. Data from twenty ocean sensors were used to train the model. Results were compared against four baseline models: two machine learning algorithms generated by robust autoML frameworks, and two deep neural networks based on CNN and LSTM, respectively. Results demonstrated ability to accurately replicate complex signals and provide comparable performance to state-of-the-art benchmarks. Learning from multiple sensors simultaneously increased robustness to missing data. This paper addresses two fundamental challenges related to environmental applications of machine learning: 1) data sparsity, particularly in a challenging ocean environment, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these at a time. Furthermore, sharing of parameters across all input steps makes SPATIAL a fast, scalable, and easily-parameterized forecasting framework.     

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.     

The emergence of leaders and followers in foraging pairs when the qualities of individuals differ

Background  

Foraging in groups offers animals a number of advantages, such as increasing their likelihood of finding food or detecting and avoiding predators. In order for a group to remain together, there has to be some degree of coordination of behaviour and movement between its members (which may in some cases be initiated by a decision-making leader, and in other cases may emerge as an underlying property of the group). For example, behavioural synchronisation is a phenomenon where animals within a group initiate and then continue to conduct identical behaviours, and has been characterised for a wide range of species. We examine how a pair of animals should behave using a state-dependent approach, and ask what conditions are likely to lead to behavioural synchronisation occurring, and whether one of the individuals is more likely to act as a leader.     

Coordination and synchronisation of anti-predation vigilance in two crane species

Much of the previous research on anti-predation vigilance in groups has assumed independent scanning for threats among group members. Alternative patterns that are based on monitoring the vigilance levels of companions can also be adaptive. Coordination of vigilance, in which foragers avoid scanning at the same time as others, should decrease the odds that no group member is alert. Synchronisation of vigilance implies that individuals are more likely to be vigilant when companions are already vigilant. While synchronisation will increase the odds that no one is vigilant, it may allow a better assessment of potential threats. We investigated temporal sequences of vigilance in family flocks consisting of two parents and at most two juveniles in two species of cranes in coastal China. We established whether the observed probability that at least one parent is alert was greater (coordination) or lower (synchronisation) than that predicted under the null hypothesis of independent vigilance. We documented coordination of vigilance in common cranes (Grus grus) foraging in an area with high potential for disturbance by people. We documented synchronisation of vigilance in red-crowned cranes (Grus japonensis) in the less but not in the more disturbed area. Coordination in small flocks leads to high collective vigilance but low foraging rates that may not be suitable in areas with low disturbance. We also argue that synchronisation should break down in areas with high disturbance because periods with low vigilance are riskier. Results highlight the view that temporal patterns of vigilance can take many forms depending on ecological factors.     

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.     

On Nash Equilibrium and Evolutionarily Stable States That Are Not Characterised by the Folk Theorem

In evolutionary game theory, evolutionarily stable states are characterised by the folk theorem because exact solutions to the replicator equation are difficult to obtain. It is generally assumed that the folk theorem, which is the fundamental theory for non-cooperative games, defines all Nash equilibria in infinitely repeated games. Here, we prove that Nash equilibria that are not characterised by the folk theorem do exist. By adopting specific reactive strategies, a group of players can be better off by coordinating their actions in repeated games. We call it a type-k equilibrium when a group of k players coordinate their actions and they have no incentive to deviate from their strategies simultaneously. The existence and stability of the type-k equilibrium in general games is discussed. This study shows that the sets of Nash equilibria and evolutionarily stable states have greater cardinality than classic game theory has predicted in many repeated games.