The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots. The work

In this paper, we present a time-discrete, incremental methodology for modeling, at the microscopic and macroscopic level, the dynamics of distributed manipulation experiments using swarms of autonomous robots endowed with reactive controllers. The methodology is well-suited for nonspatial metrics since it does not take into account robots ’ trajectories or the spatial distribution of objects in the environment. The strength of the methodology lies in the fact that it has been generated by considering incremental abstraction steps, from real robots to macroscopic models, each with well-defined mappings between successive implementation levels. Precise heuristic criteria based on geometrical considerations and systematic tests with one or two real robots prevent the introduction of free parameters in the calibration procedure of models. As a consequence, we are able to generate highly abstracted macroscopic models that can capture the dynamics of a swarm of robots at the behavioral level while still being closely anchored to the characteristics of the physical set-up. Although this methodology has been and can be applied to other experiments in distributed manipulation (e.g., object aggregation and

this paper, we present a mathematical model of an aggregation experiment carried out using multiple embodied agents in teams of time-varying sizes. The aggregation experiment is concerned with gathering and clustering of small objects initially scattered in an enclosed arena. The number of active agents engaged in the aggregation task is varying according to a local, distributed stimulus-response law, similar to the behavior observed in ant colonies. We use a set of differential equations to describe the dynamics of the system at the macroscopic level. We validate the predictions of this model by comparing them to experimental data obtained using a sensor-based embodied simulator. Results show that the proposed approach delivers accurate predictions and constitutes a computationally efficient tool for studying aggregation experiments with constant or variable group sizes. The simplicity of the model suggests that it is easily applicable to other aggregation or segregation experiments characterized by different agent capabilities and individual control algorithms

In this paper, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick pulling experiment, studied experimentally and in simulation by Ijspeert et al. [14]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through collaboration between two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), whose computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from super-linear to sub-linear performance as the number of robots is increased.

In this paper we present three scalable, fully distributed, threshold-based algorithms for allocating autonomous embodied workers to a given task whose demand evolves dynamically over time. Individuals estimate the availability of work based solely on local perceptions. The differences among the algorithms lie in the threshold distribution among teammates (homogeneous or heterogeneous team), in the mechanism used for establishing threshold values (fixed, parameter-based or variable, rule-based), and in the sharing (public) or not sharing (private) of demand estimations through local peer-to-peer communication. We tested the algorithms' efficiency and robustness in a collective manipulation case study concerned with the clustering of initially scattered small objects. The aggregation experiment has been studied at two different experimental levels using a microscopic model and embodied simulations. Results show that teams using a number of active workers dynamically controlled by one of the allocation algorithms achieve similar or better performances in aggregation than those characterized by a constant team size while using on average a considerably reduced number of agents over the whole aggregation process. While differences in efficiency among the algorithms are small, differences in robustness are much more apparent. Threshold variability and peer-to-peer communication appear to be two key mechanisms for improving worker allocation robustness against environmental perturbations.

In this paper, we discuss strengths and limitations of different abstraction levels for distributed robotics experiments. We support the discussion with a concrete case study which has been investigated at four different levels: real robots, embodied simulations, microscopic modeling, and macroscopic modeling. Both modeling methodologies presented represent the collective dynamics of the experiment as a set of stochastic events based on simple geometrical considerations and systematic tests with one or two real robots instead of computing trajectories and sensory information like an embodied simulator would do. The case study we describe is concerned with pulling sticks out of the ground - an action which requires the collaboration of two robots to be successful. Experiments were carried out with teams consisting of two to 24 individuals endowed with simple reactive controllers. In addition to showing that models can deliver both qualitatively and quantitatively correct predictions in time lapses that are three or four orders of magnitude smaller than those required by embodied simulations, we discuss differences, assumptions, and subtle numerical effects of the current modeling methodologies.

Prey retrieval, also known as foraging, is a widely used test application in collective robotics. The task consists in searching for objects spread in the environment and in bringing them to a specific place called nest. Scientific issues usually concern efficient exploration, mapping, communication among agents, task coordination and allocation, and conflict resolution. In particular, interferences among robots reduce the efficiency of the group in performing the task. Several works in the literature investigate how the control system of each robot or some form of middle/long range communication can reduce the interferences. In this work, we show that a simple adaptation mechanism, inspired by ants' behaviour and based only on information locally available to each robot, is effective in increasing the group efficiency. The same adaptation mechanism is also responsible for self-organised task allocation in the group.

We propose the ShareCam Problem: controlling a single robotic pan, tilt, zoom camera based on simultaneous frame requests from n online users. To solve it, we propose a new piecewise linear metric, Intersection Over Maximum (IOM), for the degree of satisfaction for each users. To maximize overall satisfaction, we present several algorithms. For a discrete set of m distinct zoom levels, we give an exact algorithm that runs in O(n m) time. The algorithm can be distributed to run in O(nm) time at each client and in O(n log n + mn) time at the server.

In this work, we introduce a swarm robotic system, called a swarm-bot. A swarm-bot is a self-assembling and self-organizing artifact composed of a swarm of s-bots, mobile robots with the ability to connect to/disconnect from each other. In particular, we address the problem of synthesizing controllers for the swarm-bot using Artificial Evolution. We describe the motivation behind the choice of the evolutionary approach and we provide examples of its application, detailing the results obtained in di#erent tasks, namely coordinated motion and hole avoidance. We show how evolution is able to produce simple but e#ective solutions, which lead to the emergence of self-organization in the swarm-bot.

Abstract We examine the theoretical foundations for the dynamics of puck clustering systems. Key in this investigation is the development of methods of controlling variance in cluster size, an important precursor to swarm-mediated clustering. We derive conditions under which clustering can take place in a general framework, and demonstrate two dioeerent behavioral regimes for clustering systems.