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Ultra-wideband (UWB) localization is a recent technology that performs competitively with many indoor localization methods currently available. Despite its desirable traits, such as potential high accuracy and high material penetrability, the resolution of non-line-of-sight signals remains a very hard problem and has a significant impact on the localization performance. In this work, we address the peculiarities of UWB error behavior by building models that capture the spatiality as well as the multimodal statistics of the error behavior. Our framework utilizes tessellated maps that associate probabilistic error models to localities in space. In addition to our UWB localization strategy (which provides absolute position estimates), we investigate the effects of collaboration in the form of relative positioning. To this end, we develop a relative range and bearing model and, together with the UWB model, present a unified localization technique based on a particle filter framework. We test our approach experimentally on a group of 10 mobile robots equipped with UWB emitters and extension modules providing inter-robot relative range and bearing measurements. Our experimental insights highlight the benefits of collaboration, which are consistent over numerous experimental scenarios. Also, we show the relevance, in terms of positioning accuracy, of our multimodal UWB measurement model by performing systematic comparisons with two alternative measurement models. Our final results show median localization errors below 10 cm in cluttered environments, using a modest set of 50 particles in our filter. [ABSTRACT FROM AUTHOR]

We model the dynamics of self-organized robot aggregation inspired by a study on the aggregation of gregarious arthropods. In swarms of German cockroaches, aggregation into clusters emerges solely from local interactions between the individuals, whereas the probabilities of joining or leaving a cluster are a function of the cluster size. We propose a non-spatial population dynamics model that keeps track of the number of robots in clusters of specific size using control parameters of the individual robots and the probability of detecting another robot in the environment. The model is able to quantitatively and qualitatively predict the dynamics observed in extensive realistic multi-robot simulation, and provides qualitative agreement with data obtained from aggregation of Blattela germanica larvae. In particular, we show by analysis, numerical and realistic simulation that the emergence of a single aggregate requires a minimal communication range between individuals. [ABSTRACT FROM AUTHOR]

We propose a combined spatial and non-spatial probabilistic modeling methodology motivated by an inspection task performed by a group of miniature robots. Our models explicitly consider spatiality and yield accurate predictions on system performance. An agent’s spatial distribution over time is modeled by the Fokker—Planck diffusion model and complements current non-spatial microscopic and macroscopic models that model the discrete state distribution of a distributed robotic system. We validate our models on a microscopic level based on sub-microscopic, embodied robot simulations as well as real robot experiments. Subsequently, using the validated microscopic models as our template, abstraction is raised to the level of macroscopic difference equations. We discuss the dependency of the modeling performance on the distance from the robot origin (drop-off location) and temporal convergence of the team distribution. Also, using an asymmetric setup, we show the necessity of spatial modeling methodologies for environments where the robotic platform underlies drift phenomena. [ABSTRACT FROM AUTHOR]

In this paper, we present a time-discrete, incremental methodology for modeling, at the microscopic and macroscopic levels, the dynamics of distributed manipulation experiments using swarms of autonomous robots endowed with reactive controllers. The methodology is well suited for non-spatial metrics, as it does not take into account robot 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, form 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 setup. 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 FROM AUTHOR]