A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments

Joo, Sung-Hyeon; Bae, Sung-Hun; Choi, Jun-Hyeon; Park, Hyunjin; Lee, Sangwook; You, Sujeong; Uhm, Taeyoung; Moon, Jiyoun; Kuc, Tae-Yong

doi:10.3390/electronics11152420

Cited by 3 publications

(17 citation statements)

References 48 publications

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“…Cornejo et al [38] present a complete ontology called On-toSLAM, developed to solve Simultaneous Localization and Mapping (SLAM) problems in different domains. Similar issues are also studied by Joo et al [36] in their development of a scalable navigation framework for robots in various environments and scenarios, based on the Triplet Ontological Semantic Model (TOSM). SLAM computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within, it is a crucial problem when applying automatedguided robots inside the hospital environment, e.g., for automatic drug collection and delivery (ODIN UC1).…”

Section: Discussionmentioning

confidence: 84%

“…As displayed in Table 1, each of the studies that were chosen is focused on a particular ontology and has been connected to a semantic domain in line with the review's objectives. Eight documents have been associated with the "Technology" area ( [36]- [43]) and are studies focusing on using semantic ontologies to allow and promote the management of processes through the implementation of Internet of Things, robotics and sensors. Four articles [39]- [41], [44] concern the role of semantic technologies in IoT applications and services.…”

Section: Discussionmentioning

confidence: 99%

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Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

et al. 2023

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The healthcare environment is made up of highly complicated interactions between many technologies, activities, and people. Ensuring a solid communication between them is vital to ease the healthcare management. Semantic ontologies are knowledge representation tools that implement abstractions to fully describe a given topic in terms of subjects and relations. This scoping review aims to identify and analyse available ontologies which can depict all the available use-cases that describe the hospital environment in relation to the European project ODIN and its future expansion. The review has been conducted on the Scopus database on January 13th, 2023 using the PRISMA extensions for scoping reviews. Two reviewers screened 3,225 documents emerged from the database search. Further filtering led to a final set of 32 articles to be analysed for the results. A set of 34 ontologies extracted by the identified articles has been analysed and discussed as well. The results of this study will lead to the implementation of a common integrated ontology which could hold information about healthcare entities as well as their semantic relationships, strengthen data exchange and interconnections among people, devices and applications in an expanded scenario which include Internet of Things, robots and Artificial Intelligence.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

et al. 2023

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“…A semantic map ( Galindo et al, 2005 ; Nüchter and Hertzberg, 2008 ; Deeken et al, 2015 ; Kostavelis et al, 2016 ; Ruiz-Sarmiento et al, 2017 ; Deeken et al, 2018 ; Varanka and Usery, 2018 ; Joo et al, 2022 ) annotates points and areas in a geometric map with labels that represent a symbolic relation between the annotation and the envisaged motion behavior adaptation in the annotated area ( Galindo et al, 2005 ; Nüchter and Hertzberg, 2008 ; Deeken et al, 2015 ; Kostavelis et al, 2016 ; Ruiz-Sarmiento et al, 2017 ; Deeken et al, 2018 ; Varanka and Usery, 2018 ; Joo et al, 2022 ). Well-chosen labels facilitate an application developer’s job “to program” robots, or rather, to configure their behavior.…”

Section: Related Workmentioning

confidence: 99%

“…Well-chosen labels facilitate an application developer’s job “to program” robots, or rather, to configure their behavior. In addition to the human behavior configuration, action planners have been developed, such as STRIPS ( Dornhege et al, 2010 ; Kuipers et al, 2017 ) or PDDL ( Fox and Long, 2003 ; Gerevini et al, 2009 ; Joo et al, 2022 ). These solve a constraint satisfaction problem , where the constraints are Boolean pre- and post -conditions on the status of the world.…”

Section: Related Workmentioning

confidence: 99%

“…The Task and Motion Planning literature introduces first-order logical relations between symbolically represented actions to deduce the proper sequence of actions to fulfill a task by means of STRIPS, PDDL, or PROLOG engines ( Fox and Long, 2003 ; Gerevini et al, 2009 ; Dornhege et al, 2010 ; Waibel et al, 2011 ; Kuipers et al, 2017 ; Tenorth and Beetz, 2017 ; Beetz et al, 2018 ; Faroni et al, 2020 ; Joo et al, 2022 ). We recall that it is not an objective of this paper to contribute to the improvements of such reasoning approaches and engines.…”

Section: Related Workmentioning

confidence: 99%

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Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

Chen,

Hendrikx,

Torta

et al. 2023

Front. Robot. AI

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Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots’ capabilities (its so-called “behavior semantics”) that are translated in control actions via “templates” in a “semantic map”; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an “interaction layer” that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of “collision areas”), and 3) generic application knowledge (e.g., how the robots’ behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities should result in an interaction layer with different standardized versions of semantic complexity.

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Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems

et al. 2023

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Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments.

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A Flexible Semantic Ontological Model Framework and Its Application to Robotic Navigation in Large Dynamic Environments

Cited by 3 publications

References 48 publications

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

Semantic Ontologies for Complex Healthcare Structures: A Scoping Review

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers

Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems

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