Showing papers in "Swarm Intelligence in 2011"
TL;DR: Through simulation, 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.
Abstract: 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.
115 citations
TL;DR: This paper proposes 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 and uses an opinion formation model to predict the system’s behavior.
Abstract: 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.
114 citations
TL;DR: The evolved behaviour of the collective strategy for a swarm of robots 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.
Abstract: 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.
101 citations
TL;DR: The theoretical premises and the biological basis of Swarm Cognition are discussed, a novel approach to the study of cognition as a distributed self-organising phenomenon, and novel fascinating directions for future work are pointed to.
Abstract: Basic elements of cognition have been identified in the behaviour displayed by animal collectives, ranging from honeybee swarms to human societies. For example, an insect swarm is often considered a “super-organism” that appears to exhibit cognitive behaviour as a result of the interactions among the individual insects and between the insects and the environment. Progress in disciplines such as neurosciences, cognitive psychology, social ethology and swarm intelligence has allowed researchers to recognise and model the distributed basis of cognition and to draw parallels between the behaviour of social insects and brain dynamics. In this paper, we discuss the theoretical premises and the biological basis of Swarm Cognition, a novel approach to the study of cognition as a distributed self-organising phenomenon, and we point to novel fascinating directions for future work.
77 citations
TL;DR: New findings on the workings of the mound of Macrotermes are reviewed which clarify how these remarkable structures work, and how they come to be built.
Abstract: Eusociality has evolved independently at least twice among the insects: among the Hymenoptera (ants and bees), and earlier among the Isoptera (termites). Studies of swarm intelligence, and by inference, swarm cognition, have focused largely on the bees and ants, while the termites have been relatively neglected. Yet, termites are among the world’s premier animal architects, and this betokens a sophisticated swarm intelligence capability. In this article, I review new findings on the workings of the mound of Macrotermes which clarify how these remarkable structures work, and how they come to be built. Swarm cognition in these termites is in the form of “extended” cognition, whereby the swarm’s cognitive abilities arise both from interaction amongst the individual agents within a swarm, and from the interaction of the swarm with the environment, mediated by the mound’s dynamic architecture. The latter provides large scale “cognitive maps” which enable termite swarms to assess the functional state of their structure and to guide repair efforts where necessary. The crucial role of the built environment in termite swarm cognition also points to certain “swarm cognitive disorders”, where swarms can be pushed into anomalous activities by manipulating crucial structural and functional attributes of the termite system of “extended cognition.”
69 citations
TL;DR: This paper proposes a method that allows the individual robots in the swarm to decide whether to partition the given task or not, and shows that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous.
Abstract: 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.
67 citations
TL;DR: Two different mechanisms of the slime mold’s tubular network formation process are exploited via laboratory experiments and mathematical behavior modeling to design two corresponding localized routing protocols for wireless sensor networks (WSNs) that take both efficiency and robustness into account.
Abstract: Many biological systems are composed of unreliable components which self-organize effectively into systems that achieve a balance between efficiency and robustness. One such example is the true slime mold Physarum polycephalum which is an amoeba-like organism that seeks and connects food sources and efficiently distributes nutrients throughout its cell body. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes with varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we exploit two different mechanisms of the slime mold’s tubular network formation process via laboratory experiments and mathematical behavior modeling to design two corresponding localized routing protocols for wireless sensor networks (WSNs) that take both efficiency and robustness into account. In the first mechanism of path growth, slime mold explores its immediate surroundings to discover and connect new food sources during its growth cycle. We adapt this mechanism for a path growth routing protocol by treating data sources and sinks as singular potentials to establish routes from the sinks to all the data sources. The second mechanism of path evolution is the temporal evolution of existing tubes through nonlinear feedback in order to distribute nutrients efficiently throughout the organism. Specifically, the diameters of tubes carrying large fluxes of nutrients grow to expand their capacities, and tubes that are not used decline and disappear entirely. We adapt the tube dynamics of the slime mold for a path evolution routing protocol. In our protocol, we identify one key adaptation parameter to adjust the tradeoff between efficiency and robustness of network routes. Through extensive realistic network simulations and ideal closed form or numerical computations, we validate the effectiveness of both protocols, as well as the efficiency and robustness of the resulting network connectivity.
50 citations
TL;DR: Five extensions to Ant-Miner are proposed, which incorporate stubborn ants, an ACO variation in which an ant is allowed to take into consideration its own personal past history and improve the algorithm’s performance in terms of predictive accuracy and simplicity of the generated rule set.
Abstract: Ant-Miner is an ant-based algorithm for the discovery of classification rules. This paper proposes five extensions to Ant-Miner: (1) we utilize multiple types of pheromone, one for each permitted rule class, i.e. an ant first selects the rule class and then deposits the corresponding type of pheromone; (2) we use a quality contrast intensifier to magnify the reward of high-quality rules and to penalize low-quality rules in terms of pheromone update; (3) we allow the use of a logical negation operator in the antecedents of constructed rules; (4) we incorporate stubborn ants, an ACO variation in which an ant is allowed to take into consideration its own personal past history; (5) we use an ant colony behavior in which each ant is allowed to have its own values of the α and β parameters (in a sense, to have its own personality). Empirical results on 23 datasets show improvements in the algorithm’s performance in terms of predictive accuracy and simplicity of the generated rule set.
49 citations
TL;DR: It is shown that imprecision in the nest-site selection process allows swarms to quickly reach a decision when many nest sites are available, and that the guidance mechanism of bee swarms, so-called ‘streaking’, functions both when directional dissent is present and when it is absent, making it a more general mechanism of group movement than previously thought.
Abstract: During reproductive swarming and seasonal migration, a honeybee swarm needs to locate and move to a new, suitable nest site. While the nest-site selection process in cavity-nesting species such as the European honeybee Apis mellifera is very precise with the swarm carefully selecting a single site, open-nesting species, such as Apis florea, lack such precision. These differences in precision in the nest-site selection process are thought to arise from the differing nest-site requirements of open- and cavity-nesting species. While A. florea can nest on almost any tree, A. mellifera is constrained by the scarcity of suitable nest sites. Here we show that imprecision in the nest-site selection process allows swarms to quickly reach a decision when many nest sites are available. In contrast, a very precise nest-site selection process slows down the decision-making process when nest sites are abundant. Nest-site selection in A. florea appears to be more similar to search-space sampling than to a decision-making process. Bees appear to scout the environment for general areas in which potential nest sites are abundant. Bees involved in searching the environment for suitable nest sites are also involved in guiding the swarm once the decision to depart has been made. Generally A. florea swarms exhibit a lack of consensus in the direction indicated by dancers prior to take-off. Because of this lack of consensus a swarm of A. florea will need to determine its exact direction of travel while in flight. We show that in the absence of directional consensus a swarm of bees can still be guided towards an area containing suitable nest sites provided directional dissent is not too great and nest sites are abundant. However, if the swarm needs to move to a very specific location (a single point in space), directional dissent should be avoided, resulting in a more lengthy decision-making process prior to departure. We further show that the guidance mechanism of bee swarms, so-called ‘streaking’, functions both when directional dissent is present and when it is absent, making it a more general mechanism of group movement than previously thought.
38 citations
TL;DR: In this paper, the authors propose an engineering approach based on functional blueprints, under which a system is specified in terms of desired performance and means of incrementally correcting deficiencies, and demonstrate the functional blueprint approach by applying it to integrate simplified models of tissue growth and vascularization, and further show how the composed system may itself be modulated for use as a component in more complex design.
Abstract: The engineering of grown systems poses fundamentally different system integration challenges than ordinary engineering of static designs. On the one hand, a grown system must be capable of surviving not only in its final form, but at every intermediate stage, despite the fact that its subsystems may grow unevenly or be subject to different scaling laws. On the other hand, the ability to grow offers much greater potential for adaptation, either to changes in the environment or to internal stresses developed as the system grows. I observe that the ability of subsystems to tolerate stress can be used to transform incremental adaptation into the dynamic discovery of viable growth trajectories for the system as a whole. Using this observation, I propose an engineering approach based on functional blueprints, under which a system is specified in terms of desired performance and means of incrementally correcting deficiencies. I explore how manifold geometric programming can support such an approach by simplifying the construction of distortion-tolerant programs, then demonstrate the functional blueprints approach by applying it to integrate simplified models of tissue growth and vascularization, and further show how the composed system may itself be modulated for use as a component in a more complex design.
27 citations
TL;DR: Experimental results show the ability of the model to robustly control the robot on a local navigation task, with less than 1% of the robot’s visual input being analysed, confirming the advantages of using a swarm-based system, operating in an intricate way with action selection, to judiciously control visual attention and maintain sparse spatial memories, constituting a basic form of swarm cognition.
Abstract: This paper contributes with the first validation of swarm cognition as a useful framework for the design of autonomous robots controllers. The proposed model is built upon the authors’ previous work validated on a simulated robot performing local navigation on a 2-D deterministic world. Based on the ant foraging metaphor and motivated by the multiple covert attention hypothesis, the model consists of a set of simple virtual agents inhabiting the robot’s visual input, searching in a collectively coordinated way for obstacles. Parsimonious and accurate visual attention, operating on a by-need basis, is attained by making the activity of these agents modulated by the robot’s action selection process. A by-product of the system is the maintenance of active, parallel and sparse spatial working memories. In short, the model exhibits the self-organisation of a relevant set of features composing a cognitive system. To show its robustness, the model is extended in this paper to handle the challenges of physical off-road robots equipped with noisy stereoscopic vision sensors. Furthermore, an extensive aggregate of biological arguments sustaining the model is provided. Experimental results show the ability of the model to robustly control the robot on a local navigation task, with less than 1% of the robot’s visual input being analysed. Hence, with this system the computational cost of perception is considerably reduced, thus fostering robot miniaturisation and energetic efficiency. This confirms the advantages of using a swarm-based system, operating in an intricate way with action selection, to judiciously control visual attention and maintain sparse spatial memories, constituting a basic form of swarm cognition.
TL;DR: This paper evaluates the use of reaction–diffusion for the morphogenetic engineering of distributed coordination algorithms, in particular, cluster head election in a distributed computer system, and reveals a trade-off between the Gierer–Meinhardt and Gray–Scott models.
Abstract: Chemical reaction–diffusion is a basic component of morphogenesis, and can be used to obtain interesting and unconventional self-organizing algorithms for swarms of autonomous agents, using natural or artificial chemistries. However, the performance of these algorithms in the face of disruptions has not been sufficiently studied. In this paper we evaluate the use of reaction–diffusion for the morphogenetic engineering of distributed coordination algorithms, in particular, cluster head election in a distributed computer system. We consider variants of reaction–diffusion systems around the activator–inhibitor model, able to produce spot patterns. We evaluate the robustness of these models against the deletion of activator peaks that signal the location of cluster heads, and the destruction of large patches of chemicals. Three models are selected for evaluation: the Gierer–Meinhardt Activator–Inhibitor model, the Activator–Substrate Depleted model, and the Gray–Scott model. Our results reveal a trade-off between these models. The Gierer–Meinhardt model is more stable, with rare failures, but is slower to recover from disruptions. The Gray–Scott model is able to recover more quickly, at the expense of more frequent failures. The Activator–Substrate model lies somewhere in the middle.