Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario

NOMAD spectrometer on the ExoMars trace gas orbiter mission: part 2—design, manufacturing, and testing of the ultraviolet and visible channel

NOMAD is a spectrometer suite on board the ESA/Roscosmos ExoMars Trace Gas Orbiter, which launched in March 2016. NOMAD consists of two infrared channels and one ultraviolet and visible channel, allowing the instrument to perform observations quasi-constantly, by taking nadir measurements at the day- and night-side, and during solar occultations. Here, in part 2 of a linked study, we describe the design, manufacturing, and testing of the ultraviolet and visible spectrometer channel called UVIS. We focus upon the optical design and working principle where two telescopes are coupled to a single grating spectrometer using a selector mechanism

Assessing and forecasting the performance of optimization-based design methods for robot swarms: Experimental protocol and pseudo-reality predictors

Which off-line fully-automatic optimization-based design method produces control software that will yield the best performance once executed on a swarm of physical robots? This fundamental question cannot currently be answered due to the lack of two elements: i) an appropriate experimental protocol for the evaluation and comparison of fully-automatic design methods, and ii) a procedure that reliably predicts the real-world performance of control software. This dissertation focuses on addressing this void.The literature on optimization-based design of robot swarms suffers from the absence of a clearly established state of the art. It is in fact, for the most part, a collection of feasibility studies, and little effort has been devoted to the systematic empirical evaluation and comparison of the methods or ideas proposed. Recent papers have formally characterized two approaches to the optimization-based design that were entangled in the literature: the fully-automatic and the semi-automatic approaches. In light of this novel categorization, we show that the experimental protocols employed so far for the evaluation and comparison of design methods do not respect the tenets of fully-automatic design, and we propose one that does.One of the most challenging issues when designing control software off-line on the basis of a simulation model is the reality gap: the unavoidable discrepancies between the design model and reality. It is generally understood that, because of the reality gap, the design model overestimates the performance that control software eventually yields when executed on physical robots. As a result, conducting expensive and time consuming tests on physical robots is mandatory to reliably assess control software. We introduce the concept of pseudo-reality: a simulation model, different from the one used in the design, whose purpose is to evaluate control software. With this concept, we show via a series of experiments that the reality-gap problem is to be understood as a generalization problem, akin to the one encountered in machine learning. We also use it to conceive several simulation-only predictors of real-world performance, and we assess their accuracy with a large dataset of observations collected from previous studies. Results show that the pseudo-reality predictors we propose are more accurate than the current practice for predicting the expected performance of control software for robot swarms.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms

The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-line automatic design of control software for robot swarms, as well as for single robots. It is understood that the reality gap manifests itself as a drop in performance: when control software generated in simulation is ported to physical robots, the performance observed is often disappointing compared with the one obtained in simulation. In this paper, we investigate whether, to observe the effects of the reality gap, it is necessary to assume that the control software is designed in a context that is simpler than the one in which it is evaluated. In the first experiment, we show that a performance drop may be observed also in an artificial, simulation-only reality gap: control software is generated on the basis of a simulation model and assessed on a second one. We will call this second model a pseudo-reality. We selected the simulation model to be used as a pseudo-reality by trial and error, so as to qualitatively replicate previously published observations made in experiments with physical robots. The results show that a performance drop occurs even if we can exclude that pseudo-reality is more complex than the simulation model used for the design. In the second experiment, we eliminate the trial-and-error selection of the first experiment by evaluating control software across multiple pseudo-realities, which are sampled around the original simulation model used for the design. The results of the second experiment confirm those of the first one and show that they do not depend on the specific pseudo-reality we previously selected by trial and error. Moreover, they suggest that one could use multiple pseudo-realities to evaluate automatic design methods and, from this simulation-only evaluation, infer their robustness to the reality gap.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

On Using Simulation to Predict the Performance of Robot Swarms

Abstract The discrepancy between simulation and reality–known as the reality gap–is one of the main challenges associated with using simulations to design control software for robot swarms. Currently, the reality-gap problem necessitates expensive and time consuming tests on physical robots to reliably assess control software. Predicting real-world performance accurately without recurring to physical experiments would be particularly valuable. In this paper, we compare various simulation-based predictors of the performance of robot swarms that have been proposed in the literature but never evaluated empirically. We consider (1) the classical approach adopted to estimate real-world performance, which relies on the evaluation of control software on the simulation model used in the design process, and (2) some so-called pseudo-reality predictors, which rely on simulation models other than the one used in the design process. To evaluate these predictors, we reuse 1021 instances of control software and their real-world performance gathered from seven previous studies. Results show that the pseudo-reality predictors considered yield more accurate estimates of the real-world performance than the classical approach.info:eu-repo/semantics/publishe

AutoMoDe: a modular approach to the automatic off-line design and fine-tuning of control Software for robot swarms

info:eu-repo/semantics/publishe

Automatic modular design of robot swarms based on repertoires of behaviors generated via novelty search

info:eu-repo/semantics/publishe

Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms

info:eu-repo/semantics/publishe

Concurrent design of control software and configuration of hardware for robot swarms under economic constraints

Designing a robot swarm is challenging due to its self-organized and distributed nature: complex relations exist between the behavior of the individual robots and the collective behavior that results from their interactions. In this paper, we study the concurrent automatic design of control software and the automatic configuration of the hardware of robot swarms. We introduce Waffle, a new instance of the AutoMoDe family of automatic design methods that produces control software in the form of a probabilistic finite state machine, configures the robot hardware, and selects the number of robots in the swarm. We test Waffle under economic constraints on the total monetary budget available and on the battery capacity of each individual robot comprised in the swarm. Experimental results obtained via realistic computer-based simulation on three collective missions indicate that different missions require different hardware and software configuration, and that Waffle is able to produce effective and meaningful solutions under all the experimental conditions considered.info:eu-repo/semantics/publishe

Search space for AutoMoDe-Chocolate and AutoMoDe-Maple

68187info:eu-repo/semantics/publishe