Search

In this study, we investigate the emergence of naming conventions within a swarm of robots that collectively forage, that is, collect resources from multiple sources in the environment. While foraging, the swarm explores the environment and makes a collective decision on how to exploit the available resources, either by selecting a single source or concurrently exploiting more than one. At the same time, the robots locally exchange messages in order to agree on how to name each source. Here, we study the correlation between the task-induced interaction network and the emergent naming conventions. In particular, our goal is to determine whether the dynamics of the interaction network are sufficient to determine an emergent vocabulary that is potentially useful to the robot swarm. To be useful, linguistic conventions need to be compact and meaningful, that is, to be the minimal description of the relevant features of the environment and of the made collective decision. We show that, in order to obtain a useful vocabulary, the task-dependent interaction network alone is not sufficient, but it must be combined with a correlation between language and foraging dynamics. On the basis of these results, we propose a decentralised algorithm for collective categorisation which enables the swarm to achieve a useful—compact and meaningful—naming of all the available sources. Understanding how useful linguistic conventions emerge contributes to the design of robot swarms with potentially improved autonomy, flexibility, and self-awareness. [ABSTRACT FROM AUTHOR]

Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—'Humans Go Swarming'—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence. [ABSTRACT FROM AUTHOR]

In this article, we rigorously analyze the intelligent water drops (IWD) algorithm, a metaphor-based approach for the approximate solution of discrete optimization problems proposed by Shah-Hosseini (in: Proceedings of the 2007 congress on evolutionary computation (CEC 2007), IEEE Press, Piscataway, NJ, pp 3226–3231, 2007). We demonstrate that all main algorithmic components of IWD are simplifications or special cases of ant colony optimization (ACO), and therefore, IWD is simply a particular instantiation of ACO. We show that the natural metaphor of "water drops flowing in rivers removing the soil from the riverbed", the source of inspiration of IWD, is unnecessary, misleading and based on unconvincing assumptions of river dynamics and soil erosion that lack a real scientific rationale. We carry out a detailed review of modifications and extensions proposed to IWD since its first publication in 2007. We find that research on IWD is for the most part misguided and that the vast majority of the ideas explored in the literature on IWD have been studied many years before in the context of ACO. Finally, we discuss the use of natural metaphors as a source of inspiration for optimization algorithms, which has become an extremely popular trend in the last 15 years, and propose some criteria to limit their usage to the cases in which the metaphor is indeed useful. [ABSTRACT FROM AUTHOR]

Renewable resources like fish stock or forests should be exploited at a rate that supports regeneration and sustainability—a complex problem that requires adaptive approaches to maintain a sufficiently high exploitation while avoiding depletion. In the presence of oblivious agents that cannot keep track of all available resources—a frequent condition in swarm robotics—ensuring that the exploitation effort is correctly balanced is particularly challenging. Additionally, the possibility to exploit resources by multiple robots opens the way to focusing the effort either on a single or on multiple resources in parallel. This means that the swarm needs to collectively decide whether to remain cohesive or split among multiple resources, as a function of the ability of the available resources to replenish after exploitation. In this paper, we propose a decentralised strategy for a swarm of robots that adapts to the available resources and balances the effort among them, hence allowing to maximise the exploitation rate while avoiding to completely deplete the resources. A detailed analysis of the strategy parameters provides insights into the working principles and expected performance of the robot swarm. [ABSTRACT FROM AUTHOR]

We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the Kilobot and compatible with existing hardware, the Kilogrid is a modular system composed of a grid of computing nodes, or modules that provides a bidirectional communication channel between the Kilobots and a remote workstation. In this paper, we describe the hardware and software architecture of the Kilogrid system as well as its functioning to accompany its release as a new open hardware tool for the swarm robotics community. We demonstrate the capabilities of the Kilogrid using a 200-module Kilogrid, swarms of up to 100 Kilobots, and four different case studies: exploration and obstacle avoidance, site selection based on multiple gradients, plant watering, and pheromone-based foraging. Through this set of case studies, we show how the Kilogrid allows the experimenter to virtualize sensors and actuators not available to the Kilobot and to automatize the collection of data essential for the analysis of the experiments. [ABSTRACT FROM AUTHOR]

We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human and that higher numbers of robots provoke a stronger response. [ABSTRACT FROM AUTHOR]

Achieving fast and accurate collective decisions with a large number of simple agents without relying on a central planning unit or on global communication is essential for developing complex collective behaviors. In this paper, we investigate the speed versus accuracy trade-off in collective decision-making in the context of a binary discrimination problem-i.e., how a swarm can collectively determine the best of two options. We describe a novel, fully distributed collective decision-making strategy that only requires agents with minimal capabilities and is faster than previous approaches. We evaluate our strategy experimentally, using a swarm of 100 Kilobots, and we study it theoretically, using both continuum and finite-size models. We find that the main factor affecting the speed versus accuracy trade-off of our strategy is the agents' neighborhood size-i.e., the number of agents with whom the current opinion of each agent is shared. The proposed strategy and the associated theoretical framework can be used to design swarms that take collective decisions at a given level of speed and/or accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we study how to obtain a quantitative correspondence between the dynamics of the microscopic implementation of a robot swarm and the dynamics of a macroscopic model of nest-site selection in honeybees. We do so by considering a collective decision-making case study: the shortest path discovery/selection problem. In this case study, obtaining a quantitative correspondence between the microscopic and macroscopic dynamics-the so-called micro-macro link problem-is particularly challenging because the macroscopic model does not take into account the spatial factors inherent to the path discovery/selection problem. We frame this study in the context of a general engineering methodology that prescribes the inclusion of available theoretical knowledge about target macroscopic models into design patterns for the microscopic implementation. The attainment of the micro-macro link presented in this paper represents a necessary step towards the formalisation of a design pattern for collective decision making in distributed systems. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with stochastic demands and customers (VRPSDC) requires finding the optimal route for a capacitated vehicle that delivers goods to a set of customers, where each customer has a fixed probability of requiring being visited and a stochastic demand. For large instances, the evaluation of the cost function is a primary bottleneck when searching for high quality solutions within a limited computation time. We tackle this issue by using an empirical estimation approach. Moreover, we adopt a recently developed state-of-the-art iterative improvement algorithm for the closely related probabilistic traveling salesman problem. We integrate these two components into several metaheuristics and we show that they outperform substantially the current best algorithm for this problem. [ABSTRACT FROM AUTHOR]

Spatially targeted communication (STC) allows a message sender to choose message recipients based on their location in space. Currently, STC in multirobot systems is limited to centralized systems. In this paper, we propose a novel communication protocol that enables STC in decentralized multirobot systems. The proposed protocol dispenses with the many aspects that underpin previous approaches, including external tracking infrastructure, a priori knowledge, global information, dedicated communication devices or unique robot IDs. We show how off-the-shelf hardware components such as cameras and LEDs can be used to establish ad-hoc STC links between robots. We present a Markov chain model for each of the two constituent parts of our proposed protocol and we show, using both model-based analysis and experimentation, that the proposed protocol is highly scalable. We also present the results of extensive experiments carried out on an autonomous, heterogeneous multirobot system composed of one aerial robot and numerous ground-based robots. Finally, two real world application scenarios are presented in which we show how spatial coordination can be achieved in a decentralized multirobot system through STC. [ABSTRACT FROM AUTHOR]

Research in swarm robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations-no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in swarm robotics research. This approach is based on a physical device called the 'task abstraction module' (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck-research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a swarm of e-puck robots. [ABSTRACT FROM AUTHOR]