Efficiency and optimization of explicit and implicit communication schemes in collaborative robotics experiments

Easton, Kjerstin; Martinoli, Alcherio

doi:10.1109/irds.2002.1041693

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…For instance, in the stick pulling experiment, the introduction of directional communication for attracting robots 18,8 and vision capabilities in a limited cone of view 8 generates additional difficulties for a nonspatial modeling methodology such as that presented in this paper. Usually, quantitatively correct predictions using nonspatial, probabilistic models can still be achieved, but not without using free parameters 18 .…”

Section: Nonspatial Modelsmentioning

confidence: 99%

Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation

Martinoli¹,

Easton

Agassounon

2004

The International Journal of Robotics Research

235

267

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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.

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Section: Nonspatial Modelsmentioning

confidence: 99%

Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation

Martinoli¹,

Easton

Agassounon

2004

The International Journal of Robotics Research

235

267

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“…Jones and Mataric (2003) introduce the notion of "passive" as opposed to "active" communication between robots, allowing their "minimalist" robots to passively signal their current foraging state by means of a coloured beacon. Easton and Martinoli (2002) compared the effect of implicit (mechanical interaction, or vision) and explicit (infrared signalling) on the rate of successful collaborations in an "embodied" simulator. And Ijspeert et al (2001) found that better performance in their stick pulling experiments was achieved when robots were able to use directional IR emitters and receivers in a simple signalling scheme to indicate when they had gripped a stick.…”

Section: Separating the Various Swarm Areasmentioning

confidence: 99%

Swarm robotics and minimalism

Sharkey

2007

Connection Science

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Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralised local control and communication. Earlier studies went a step further in emphasising the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes sub-areas of SR based on the emphases and justifications for minimalism and individual simplicity.

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“…Getting simulations to predict performance in single robot experiments is challenging. Models do not always capture important factors such as sensor to environment interactions [1,2] and hardware inconsistencies [3,4], causing simulations to perform better or worse than in the real world. This effect is magnified when using more than one robot in a team.…”

Section: Introductionmentioning

confidence: 99%

Identification of Issues in Predicting Multi-Robot Performance through Model-Based Simulations

Dawson¹,

Wellman²,

Anderson³

2011

ICA

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Predicting the performance of intelligent multi-robot systems is advantageous because running physical experiments with teams of robots can be costly and time consuming. Controlling for every factor can be difficult in the presence of minor disparities (i.e. battery charge). Access to a variety of environmental configurations and hardware choices is prohibitive in many cases. With the eminent need for dependable robot controllers and algorithms, it is essential to understand when real robot performance can be accurately predicted. New prediction methods must account for the effects of digital and physical interaction between the robots that are more complex than just collision detection of 2D or physics-based 3D models. In this paper, we identify issues in predicting multi-robot performance and present examples of statistical and model-based simulation methods and their applicability to multi-robot systems. Even when sensor noise, latency and environmental configuration are modeled in some complexity, multi-robot systems interject interference and messaging latency, causing many prediction systems to fail to correlate to absolute or relative performance. We support this supposition by comparing results from 3D physics-based simulations to identical experiments with a physical robot team for a coverage task

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Efficiency and optimization of explicit and implicit communication schemes in collaborative robotics experiments

Cited by 8 publications

References 9 publications

Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation

Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation

Swarm robotics and minimalism

Identification of Issues in Predicting Multi-Robot Performance through Model-Based Simulations

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