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.     

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.     

Task partitioning in swarms of robots: an adaptive method for strategy selection

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals?? skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.     

Chemical trail systems,orientation, and territorial interactions in the antLasius neoniger

Foraging and territoriality in the ant Lasius neonigerinvolves a series of trails which channel foragers away from adjacent colonies. Experimental studies suggest that the trails are composed of colony-specific, persistent orientation components of hindgut material that accumulate on trails during foraging. A less durable component of the hindgut trail pheromone regulates recruitment. Foraging directionality and the use of a trail could be modified by experimentally arranging confrontations with conspecifics. The orientation of foragers is mediated by visual as well as chemical cues. Components of the foraging and territorial system of L. neonigerappear to include (1) a network of subnests which change in position seasonally within each polydomous nest; (2) a series of trails emanating from each subnest that adjusts search toward resource patches and away from aggressive, neighboring conspecifics; and (3) trail communication involving an ephemeral component of the hindgut trail pheromone that regulates the organization of cooperative prey retrieval and a more persistent component that serves as an orientation guide.     

Trail Communication During Foraging and Recruitment in the Subterranean Termite Reticulitermes santonensis De Feytaud (Isoptera, Rhinotermitidae)

The search for food in the French subterranean termite Reticulitermes santonensis De Feytaud is organized in part by chemical trails laid with the secretion of their abdominal sternal gland. Trail-laying and -following behavior of R. santonensis was investigated in bioassays. During foraging for food termites walk slowly (on average, 2.3 mm/s) and lay a dotted trail by dabbing the abdomen at intervals on the ground. When food is discovered they return at a quick pace (on average, 8.9 mm/s) to the nest, laying a trail for recruiting nestmates to the food source. While laying this recruitment trail the workers drag the abdomen continuously on the ground. The recruitment trail is highly attractive: it is followed within a few seconds, by more nestmates, and at a quicker pace (on average, 6.4 mm/s) than foraging trails (on average, 2.9 mm/s). The difference between foraging and recruitment trails in R. santonensis could be attributed to different quantities of trail pheromone. A caste-specific difference in trail pheromone thresholds, with workers of R. santonensis being more sensitive to trails than soldiers, was also documented: soldiers respond only to trails with a high concentration of trail pheromone.     

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.     

Coordination of Raiding and Emigration in the Ponerine Army Ant Leptogenys distinguenda (Hymenoptera: Formicidae: Ponerinae): A Signal Analysis

Several glandular sources of trail pheromones have been discovered in army ants in general. Nevertheless, at present the understanding of the highly coordinated behavior of these ants is far from complete. The importance of trail pheromone communication for the coordination of raids and emigrations in the ponerine army ant Leptogenys distinguenda was examined, and its ecological function is discussed. The secretions of at least two glands organize the swarming activities of L. distinguenda. The pygidial gland is the source of an orientation pheromone holding the group of raiding workers together. The same pheromone guides emigrations to new nest sites. In addition, the poison sac contains two further components: one with a weak orientation effect and another which produces strong, but short-term attraction and excitement. The latter component is important in prey recruitment and characterizes raid trails. This highly volatile recruitment pheromone allows the extreme swarm dynamic characteristic of this species. Emigration trails lack the poison gland secretion. Due to their different chemical compositions, the ants are thus able to distinguish between raid and emigration trails. Nest emigration is not induced chemically, but mechanically, by the jerking movements of stimulating workers.     

Self-organized cooperation between robotic swarms

We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.     

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.     

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.     

The Regulation of Ant Colony Foraging Activity without Spatial Information

Many dynamical networks, such as the ones that produce the collective behavior of social insects, operate without any central control, instead arising from local interactions among individuals. A well-studied example is the formation of recruitment trails in ant colonies, but many ant species do not use pheromone trails. We present a model of the regulation of foraging by harvester ant (Pogonomyrmex barbatus) colonies. This species forages for scattered seeds that one ant can retrieve on its own, so there is no need for spatial information such as pheromone trails that lead ants to specific locations. Previous work shows that colony foraging activity, the rate at which ants go out to search individually for seeds, is regulated in response to current food availability throughout the colony's foraging area. Ants use the rate of brief antennal contacts inside the nest between foragers returning with food and outgoing foragers available to leave the nest on the next foraging trip. Here we present a feedback-based algorithm that captures the main features of data from field experiments in which the rate of returning foragers was manipulated. The algorithm draws on our finding that the distribution of intervals between successive ants returning to the nest is a Poisson process. We fitted the parameter that estimates the effect of each returning forager on the rate at which outgoing foragers leave the nest. We found that correlations between observed rates of returning foragers and simulated rates of outgoing foragers, using our model, were similar to those in the data. Our simple stochastic model shows how the regulation of ant colony foraging can operate without spatial information, describing a process at the level of individual ants that predicts the overall foraging activity of the colony.     

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.     

Information flow principles for plasticity in foraging robot swarms

An important characteristic of a robot swarm that must operate in the real world is the ability to cope with changeable environments by exhibiting behavioural plasticity at the collective level. For example, a swarm of foraging robots should be able to repeatedly reorganise in order to exploit resource deposits that appear intermittently in different locations throughout their environment. In this paper, we report on simulation experiments with homogeneous foraging robot teams and show that analysing swarm behaviour in terms of information flow can help us to identify whether a particular behavioural strategy is likely to exhibit useful swarm plasticity in response to dynamic environments. While it is beneficial to maximise the rate at which robots share information when they make collective decisions in a static environment, plastic swarm behaviour in changeable environments requires regulated information transfer in order to achieve a balance between the exploitation of existing information and exploration leading to acquisition of new information. We give examples of how information flow analysis can help designers to decide on robot control strategies with relevance to a number of applications explored in the swarm robotics literature.     

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.     

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.     

Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed

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

The Trail Less Traveled: Individual Decision-Making and Its Effect on Group Behavior

Social insect colonies are complex systems in which the interactions of many individuals lead to colony-level collective behaviors such as foraging. However, the emergent properties of collective behaviors may not necessarily be adaptive. Here, we examine symmetry breaking, an emergent pattern exhibited by some social insects that can lead colonies to focus their foraging effort on only one of several available food patches. Symmetry breaking has been reported to occur in several ant species. However, it is not clear whether it arises as an unavoidable epiphenomenon of pheromone recruitment, or whether it is an adaptive behavior that can be controlled through modification of the individual behavior of workers. In this paper, we used a simulation model to test how symmetry breaking is affected by the degree of non-linearity of recruitment, the specific mechanism used by individuals to choose between patches, patch size, and forager number. The model shows that foraging intensity on different trails becomes increasingly asymmetric as the recruitment response of individuals varies from linear to highly non-linear, supporting the predictions of previous work. Surprisingly, we also found that the direction of the relationship between forager number (i.e., colony size) and asymmetry varied depending on the specific details of the decision rule used by individuals. Limiting the size of the resource produced a damping effect on asymmetry, but only at high forager numbers. Variation in the rule used by individual ants to choose trails is a likely mechanism that could cause variation among the foraging behaviors of species, and is a behavior upon which selection could act.     

The effect of a non-uniform turning kernel on ant trail morphology

An ordinary differential equation model is constructed for the formation of pheromone trails by ants on a pre-determined network. At each junction of the trails the probability that an ant will turn through any particular angle is given by a turning kernel. We prove analytically using analogies with thermodynamics that turning behaviour determines trail morphology when the turning kernel is steep. We conjecture that this is also true in general for non-uniform turning kernels and present numerical simulations as evidence. Using this conjecture we show the existence of three types of collective foraging: individuals exploring without the use of a trail network, and two distinct types of trail networks; one that consists of low pheromone concentration trails that bend, branch and dissipate and one that consists of high pheromone concentration, straight, unbranched trails. We show that the form of the pheromone response function is crucial in determining the existence and stability of the steady states corresponding to these three foraging strategies, and examine the bifurcations between different trail morphologies as a function of turning kernel steepness for a particular response function.Revised version: 25 December 2002     

The Role of the Vibration Signal during Nest‐Site Selection by Honey Bee Swarms

The vibration signal may influence nest‐site selection by honey bee swarms by enhancing scouting and recruitment. We investigated this hypothesis by comparing (1) the number of nest sites and the distances communicated by nest‐site dancers on swarms from which vibrators were and were not removed and (2) the behavior of scouts visiting higher‐quality (HQ) and lower‐quality (LQ) nest boxes. The removal of vibrators from swarms did not alter the number of nest sites investigated, the distances traveled to nest sites, or the time required to select a new nest cavity. In contrast, vibrator removal tripled the time required for swarms to achieve liftoff after a cavity had been selected, although all swarm eventually became airborne and moved to a new site. About 14% of the scouts that visited the HQ and LQ nest boxes performed vibration signals; however, nest‐box quality did not influence the tendency to produce the signal or intermix vibration signals and recruitment dances. However, we did find a significant, positive correlation between overall levels of vibration signal activity and nest‐site recruitment during the house‐hunting process. When viewed in concert, our results suggest that the vibration signal contributes to the house‐hunting process by operating in a non‐specific manner that may enhance scouting and recruitment in general during nest‐site selection and facilitate rapid swarm liftoff after a new nest site has been chosen. The vibration signal is therefore a component in the cascade of communication signals that orchestrate house‐hunting and colony relocation decisions.