Sensor coverage robot swarms using local sensing without metric information

Ramaithitima, Rattanachai; Whitzer, Michael; Bhattacharya, Subhrajit; Kumar, Vijay

doi:10.1109/icra.2015.7139670

Cited by 29 publications

(27 citation statements)

References 14 publications

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“…The ability to locally measure the relative distances and bearings of neighboring robots is fundamental for collective behavior in swarm robotics. [98][99][100][101] For mROBerTOs, the swarm-sensing module, with IR emitters and detectors, was specifically designed and implemented for this purpose. mROBerTO sends out modulated IR signals via its IR emitters, achieved using the PWM feature on the ATmega328P microcontroller.…”

Section: Appendix C: Sensing and Communication For Collective Behaviormentioning

confidence: 99%

Design and implementation of a millirobot for swarm studies –mROBerTO

Kim¹,

Kashino²,

Colaco³

et al. 2018

Robotica

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SUMMARYThe use of millirobots, particularly in swarm studies, would enable researchers to verify their proposed autonomous cooperative behavior algorithms under realistic conditions with a large number of agents. While multiple designs for such robots have been proposed, they, typically, require custom-made components, which make replication and manufacturing difficult, and, mostly, employ non-modular integral designs. Furthermore, these robots' proposed small sizes tend to limit sensory perception capabilities and operational time. Some have resolved few of the above issues through the use of extensions that, unfortunately, add to their size.In contribution to the pertinent field, thus, a novel millirobot with an open-source design, addressing the above concerns, is presented in this paper. Our proposed millirobot has a modular design and uses easy to source, off-the-shelf components. Themilli-robot-Toronto (mROBerTO) also includes a variety of sensors and has a 16 × 16 mm2footprint.mROBerTO's wireless communication capabilities include ANT™, Bluetooth Smart, or both simultaneously. Data-processing is handled by an ARM processor with 256 KB of flash memory. Additionally, the sensing modules allow for extending or changing the robot's perception capabilities without adding to the robot's size. For example, the swarm-sensing module, designed to facilitate swarm studies, allows for measuring proximity and bearing to neighboring robots and performing local communications.Extensive experiments, some of which are presented herein, have illustrated the capability ofmROBerTOunits for use in implementing a variety of commonly proposed swarm algorithms.

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Section: Appendix C: Sensing and Communication For Collective Behaviormentioning

confidence: 99%

Design and implementation of a millirobot for swarm studies –mROBerTO

Kim¹,

Kashino²,

Colaco³

et al. 2018

Robotica

View full text Add to dashboard Cite

show abstract

“…However, these studies focused on stationary networks, and are mainly concerned about the coverage holes in the sensing domain of the network rather than the characterization of the physical environment itself. Ramaithitima et al [44] consider the coverage of unknown environment under noisy and limited sensing information and no metric or localization information. Although this work is closely related to ours in terms of the sensing and localization constraints and mathematical tools, it differs in the sense that it focuses on sensor deployment rather than mapping.…”

Section: Related Workmentioning

confidence: 99%

A framework for mapping with biobotic insect networks: From local to global maps

Dirafzoon

Bozkurt

Lobatón

2017

Robotics and Autonomous Systems

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We present an approach for global exploration and mapping of unknown environments using a swarm of cyborg insects, known as biobots, for emergency response scenarios under minimal sensing and localization constraints. We exploit natural stochastic motion models and controlled locomotion of biobots in conjunction with an aerial leader to explore and map a domain of interest. A sliding window strategy is adopted to construct local maps from coordinate free encounter information of the agents by means of local metric estimation. Robust topological features from these local representations are extracted using topological data analysis and a classification scheme. These maps are then merged into a global map which can be visualized using a graphical representation, that integrates geometric as well as topological features of the environment. Simulation and experimental results with biologically inspired robotic platform are presented to illustrate and verify the correctness of our approach, which provides building blocks for SLAM with biobotic insects.

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“…The bottleneck distances between the persistence diagrams in (17) and (18) are bounded by the errors that correspond to the summation of geometric estimation error and sampling error. In (17) the term λ 3 δ l corresponds to the metric estimation error between landmarks, which will vanish if λ 3 → 0 as discussed in the previous section.…”

Section: A Topological Stability Analysismentioning

confidence: 99%

Geometric Learning and Topological Inference With Biobotic Networks

Dirafzoon

Bozkurt

Lobatón

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinatefree local interactions among them to infer geometric and topological information about the environment, under minimal sensing and localization constraints. Local interactions are used to create a graphical representation referred to as the encounter graph. A metric is estimated over the encounter graph of the agents in order to construct a geometric point cloud using manifold learning techniques. Topological data analysis (TDA), in particular persistent homology, is used in order to extract topological features of the space and a classification method is proposed to infer robust features of interest (e.g. existence of obstacles). We examine the asymptotic behavior of the proposed metric in terms of the convergence to the geodesic distances in the underlying manifold of the domain, and provide stability analysis results for the topological persistence. The proposed framework and its convergences and stability analysis are demonstrated through numerical simulations and experiments.

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Sensor coverage robot swarms using local sensing without metric information

Cited by 29 publications

References 14 publications

Design and implementation of a millirobot for swarm studies –mROBerTO

Design and implementation of a millirobot for swarm studies –mROBerTO

A framework for mapping with biobotic insect networks: From local to global maps

Geometric Learning and Topological Inference With Biobotic Networks

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