Collective behavior based on self-organization has been shown in group-living animals from insects to vertebrates. These findings have stimulated engineers to investigate approaches for the coordination of autonomous multirobot systems based on self-organization. In this experimental study, we show collective decision-making by mixed groups of cockroaches and socially integrated autonomous robots, leading to shared shelter selection. Individuals, natural or artificial, are perceived as equivalent, and the collective decision emerges from nonlinear feedbacks based on local interactions. Even when in the minority, robots can modulate the collective decision-making process and produce a global pattern not observed in their absence. These results demonstrate the possibility of using intelligent autonomous devices to study and control self-organized behavioral patterns in group-living animals.

This paper presents an investigation of odor localization by groups of autonomous mobile robots. First, we describe a distributed algorithm by which groups of agents can solve the full odor localization task. Next, we establish that conducting polymerbased odor sensors possess the combination of speed and sensitivity necessary to enable real world odor plume tracing and we demonstrate that simple local position, odor, and flow information, tightly coupled with robot behavior, is sufficient to allow a robot to localize the source of an odor plume. Finally, we show that elementary communication among a group of agents can increase the efficiency of the odor localization system performance.

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 segregation, foraging), in this paper we focus on a strictly collaborative case study concerned with pulling sticks out of the ground, an action that requires the collaboration of two robots to be successful. Experiments were carried out with teams consisting of two to 600 individuals at different levels of implementation (real robots, embodied simulations, microscopic and macroscopic models). Results show that models can deliver both qualitatively and quantitatively correct predictions in time lapses that are at least four orders of magnitude smaller than those required by embodied simulations and that they represent a useful tool for generalizing the dynamics of these highly stochastic, asynchronous, nonlinear systems, often outperforming intuitive reasoning. Finally, in addition to discussing subtle numerical effects, small prediction discrepancies, and difficulties in generating the mapping between different abstractions levels, we conclude the paper by reviewing the intrinsic limitations of the current modeling methodology and by proposing a few suggestions for future work.

Abstract-Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm Optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by Particle Swarm Optimization. Additionally, we exploit this inspiration by modifying the Particle Swarm Optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range.

In this article, 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. [Autonomous Robots,11,. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which 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 superlinear to sublinear performance as the number of robots is increased.

Abstract. In this paper, we review methods used for macroscopic modeling and analyzing collective behavior of swarm robotic systems. Although the behavior of an individual robot in a swarm is often characterized by an important stochastic component, the collective behavior of swarms is statistically predictable and has often a simple probabilistic description. Indeed, we show that a class of mathematical models that describe the dynamics of collective behavior can be generated using the individual robot controller as modeling blueprint. We illustrate the macroscopic modelling methods with the help of a few sample results gathered in distributed manipulation experiments (collaborative stick pulling, foraging, aggregation). We compare the models' predictions to results of probabilistic numeric and sensor-based simulations as well as experiments with real robots. Depending on the assumptions, the metric used, and the complexity of the models, we show that it is possible to achieve quantitatively correct predictions. VisionSwarm Robotics is an emerging area in collective robotics which uses a fully distributed control paradigm and relatively simple robots to achieve coordinated behavior at the group level. Swarm robotic systems are self-organizing, meaning that constructive collective (or macroscopic) behavior emerges from individual (or microscopic) decisions robots make. These decisions are based on purely local information that comes from other robots as well as the environment. Swarm Robotics takes its inspiration from examples of collective behavior exhibited by biological systems, such as social insects [3], and the swarming, flocking, herding, and shoaling phenomena in vertebrates. In all these systems, the abilities of the collective appear to transcend the abilities of the constituent individuals.The main advantages of the application of the swarm approach to the control of a group of robots are: (i) scalability: the control architecture can be kept exactly the same from a few units to thousands of units; (ii) flexibility: units can be dynamically added or removed, they can be given the ability to reallocate

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Abstract-Vision-based tracking is used in nearly all robotic laboratories for monitoring and extracting of agent positions, orientations, and trajectories. However, there is currently no accepted standard software solution available, so many research groups resort to developing and using their own custom software. In this paper, we present Version 4 of SwisTrack, an open source project for simultaneous tracking of multiple agents. While its broad range of pre-implemented algorithmic components allows it to be used in a variety of experimental applications, its novelty stands in its highly modular architecture. Advanced users can therefore also implement additional customized modules which extend the functionality of the existing components within the provided interface. This paper introduces SwisTrack and shows experiments with both marked and marker-less agents.