Distributed management and representation of data and context in robotic applications

Dietrich, André; Zug, Sebastian; Mohammad, Siba; Kaiser, Jörg

doi:10.1109/iros.2014.6942700

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…28 Some techniques must be put in place to allow robots to query these distributed data. Dietrich et al 29 propose to use local databases, one in each entity, where data are organized in a spatial hierarchy, for example, an object has a position relative to a robot, the robot is positioned in a room and so on. Other authors 30,31 propose that robots send specific observation requests to the distributed entities, for example, a region and objects of interest: this may speed up otherwise intractable sensor processing problems (see Figure 3).…”

Section: Perception Abilitymentioning

confidence: 99%

The Internet of Robotic Things

Simoens

Dragone

Saffiotti

2018

International Journal of Advanced Robotic Systems

155

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The Internet of Robotic Things is an emerging vision that brings together pervasive sensors and objects with robotic and autonomous systems. This survey examines how the merger of robotic and Internet of Things technologies will advance the abilities of both the current Internet of Things and the current robotic systems, thus enabling the creation of new, potentially disruptive services. We discuss some of the new technological challenges created by this merger and conclude that a truly holistic view is needed but currently lacking.

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Section: Perception Abilitymentioning

confidence: 99%

The Internet of Robotic Things

Simoens

Dragone

Saffiotti

2018

International Journal of Advanced Robotic Systems

155

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“…SelectScript is part of a larger research effort, whereby we try to make all information origination from an ecology of smart systems accessible and queryable. In [19] and [20] we had described a methodology that overlays smart and distributed environments with some kind of distributed scene graph (by using cloud based techniques). This enables us to organize and access these environments in a way that world models (for a certain area) on the basis of OpenRAVE can be reconstructed.…”

Section: If( Condition ;mentioning

confidence: 99%

Reasoning in complex environments with the SelectScript declarative language

Dietrich¹,

Zug²,

Nardi³

et al. 2015

Preprint

Self Cite

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SelectScript is an extendable, adaptable, and declarative domainspecific language aimed at information retrieval from simulation environments and robotic world models in an SQL-like manner. In this work we have extended the language in two directions. First, we have implemented hierarchical queries; second, we improve efficiency enabling manual design space exploration on different "search" strategies. As depicted in the teaser above, we demonstrate the applicability of such extensions in two application problems; the basic language concepts are explained by solving the classical problem of the Towers of Hanoi and then a common path planning problem in a complex 3D environment is implemented.

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“…Apart from providing a common ground for knowledge sharing supporting efficient collaboration among robots, knowledge management systems allow for information reuse 11 …”

Section: Introductionmentioning

confidence: 99%

“…8,9 Information sharing cannot take place if it does not exist a communication platform, common to all the robots, that is, a knowledge management structure, in which a language for representing knowledge, a mechanism for knowledge association, a protocol for information exchange and, finally, a memory system are well established. [10][11][12][13] Languages for representing knowledge in multirobot systems are commonly based on beliefs intentions, 14 semantic networks, 15 frame languages, 16 and resource description frameworks. 17,18 Knowledge association is responsible of the bidirectional information flow, where low-level data is passed upward and the high-level information is returned downwards using logical inference, 19 Bayesian inference, 14 semantic relationships and hierarchies, 20 or computational learning methods.…”

Section: Introductionmentioning

confidence: 99%

Role and task allocation framework for Multirobot collaboration with latent knowledge estimation

Gianni

Uddin

2020

Engineering Reports

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In this work a novel framework for modeling role and task allocation in Cooperative Heterogeneous Multi-Robot Systems (CHMRSs) is presented. This framework encodes a CHMRS as a set of multidimensional relational structures (MDRSs). This set of structure defines collaborative tasks through both temporal and spatial relations between processes of heterogeneous robots. These relations are enriched with tensors which allow for geometrical reasoning about collaborative tasks. A learning schema is also proposed in order to derive the components of each MDRS. According to this schema, the components are learnt from data reporting the situated history of the processes executed by the team of robots. Data are organized as a multirobot collaboration treebank (MRCT) in order to support learning. Moreover, a generative approach, based on a probabilistic model, is combined together with nonnegative tensor decomposition (NTD) for both building the tensors and estimating latent knowledge. Preliminary evaluation of the performance of this framework is performed in simulation with three heterogeneous robots, namely, two Unmanned Ground Vehicles (UGVs) and one Unmanned Aerial Vehicle (UAV).

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Distributed management and representation of data and context in robotic applications

Cited by 8 publications

References 24 publications

The Internet of Robotic Things

The Internet of Robotic Things

Reasoning in complex environments with the SelectScript declarative language

Role and task allocation framework for Multirobot collaboration with latent knowledge estimation

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