Representations for robot knowledge in the KnowRob framework

Tenorth, Moritz; Beetz, Michael

doi:10.1016/j.artint.2015.05.010

Cited by 130 publications

(101 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a component to this project, Tenorth et. al [44,50,51] presented KnowRob (illustrated in Figure 4) as a knowledge processing system for querying the openEASE knowledge base using Prolog predicates. KnowRob combines various sources of knowledge such as web pages (methods from instructional websites, images of 4 openEASEhttp://www.open-ease.org/ usable objects, etc.…”

Section: Distributive and Collaborative Representationsmentioning

confidence: 99%

A Survey of Knowledge Representation in Service Robotics

Paulius

Sun

2019

Robotics and Autonomous Systems

View full text Add to dashboard Cite

Section: Distributive and Collaborative Representationsmentioning

confidence: 99%

A Survey of Knowledge Representation in Service Robotics

Paulius

Sun

2019

Robotics and Autonomous Systems

View full text Add to dashboard Cite

“…The ontology proposed by Insaurralde et al [13] also lacks the inclusion of environmental context, though it provides a good representation of planning and control systems for AUVs. The KnowRob [14] knowledge processing framework developed a set of ontologies to abstract robot actions, events, objects, environments, and the robot’s hardware as well as inference procedures that operate on this common representation. The KnowRob puts its emphasis on improving the autonomy of individual vehicles instead of enabling the cooperation and coordination of multiple vehicles.…”

Section: Related Workmentioning

confidence: 99%

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Bilbao

Martín-Wanton

et al. 2017

Sensors

View full text Add to dashboard Cite

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

show abstract

“…Finally, [6] discusses how one can structure a knowledge base for robots for goal achievement that combines different knowledge types, thus addressing RC4. The authors propose a combination of different knowledge areas, different knowledge sources and different inference mechanisms to cover the breadth and depth of required knowledge and inferences.…”

Section: Papers Focusing On Ca1: Robots That Knowmentioning

confidence: 99%

Towards a science of integrated AI and Robotics

Rajan

Saffiotti

2017

Artificial Intelligence

114

View full text Add to dashboard Cite

The early promise of the impact of machine intelligence did not involve the partitioning of the nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded intelligence as being conjoined between perception, reasoning and actuation. Yet over the years the fields of AI and Robotics drifted apart. Practitioners of AI focused on problems and algorithms abstracted from the real world. Roboticists, generally with a background in mechanical and electrical engineering, concentrated on sensorimotor functions. That divergence is slowly being bridged with the maturity of both fields and with the growing interest in autonomous systems. This special issue brings together the state of the art and practice of the emergent field of integrated AI and Robotics, and highlights the key areas along which this current evolution of machine intelligence is heading.

show abstract

Representations for robot knowledge in the KnowRob framework

Cited by 130 publications

References 22 publications

A Survey of Knowledge Representation in Service Robotics

A Survey of Knowledge Representation in Service Robotics

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Towards a science of integrated AI and Robotics

Contact Info

Product

Resources

About