scholarly journals OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

Robotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 125
Author(s):  
Maria A. Cornejo-Lupa ◽  
Yudith Cardinale ◽  
Regina Ticona-Herrera ◽  
Dennis Barrios-Aranibar ◽  
Manoel Andrade ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Autonomous Robots ◽  
State Of The Art ◽  
Formal Representation ◽  
Robot Operating System ◽  
Localization And Mapping ◽  
Complex Knowledge ◽  
Structural Levels ◽  
Mapping Information

Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.

The Next Generation of Personalization Techniques

2011 ◽  
pp. 72-92
Author(s):  
Gulden Uchyigit
Keyword(s):  
Knowledge Representation ◽  
Traditional Method ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
User Preferences ◽  
Future Trends ◽  
User Profiles ◽  
User Interests ◽  
Key Concepts

Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization approaches model user preferences and store them in user profiles, used to deliver personalized content. A traditional method for profile representation is the so called keyword-based representation, where the user interests are modelled using keywords which are selected from the contents of the items which the user has rated. Although, keyword based approaches are simple and are extensively used for profile representation they fail to represent semantic-based information, this information is lost during the pre-processing phase. Future trends in personalization systems necessitate more innovative personalization techniques that are able to capture rich semanticbased information during the representation, modelling and learning phases. In recent years ontologies (key concepts and along with their interrelationships) to express semantic-based information have been very popular in domain knowledge representation. The primary goal of this chapter is to present an overview of the state-of-the art techniques and methodologies which aim to integrate personalization technologies with semantic-based information.

Research of Maize Field Knowledge Representation Building Based on Ontology

2012 ◽  
Vol 546-547 ◽  
pp. 441-445
Author(s):  
Ying Zhang ◽  
Gui Fen Chen
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Intelligent Systems ◽  
Domain Knowledge ◽  
Semantic Level ◽  
Maize Field ◽  
Complex Knowledge ◽  
The Relationship ◽  
Knowledge Ontology ◽  
Knowledge Hierarchy

The knowledge representation of the traditional artificial intelligence used different modeling methods and the different development tools, it led to the lack of interoperability between all kinds of knowledge, ontology solved the problem. Ontology, which is a model in semantic and knowledge hierarchy describing the concept and the relationship between the concepts, has been the focus of the field of artificial intelligence since it was proposed. This paper explored the knowledge representation based on ontology in the field of artificial intelligence, built the maize domain knowledge ontology, the result shows: ontology can effectively solve the heterogeneous problem of expression of complex knowledge, makes the computer to understand information for the semantic level, and benefit to develop the intelligent systems of maize.

Logical Inference Based on Incomplete and/or Fuzzy Ontologies

2011 ◽  
pp. 1-16 ◽  
Author(s):  
Juliusz L. Kulikowski
Keyword(s):  
Decision Making ◽  
Knowledge Representation ◽  
Domain Knowledge ◽  
Computer Systems ◽  
Logical Inference ◽  
Formal Representation ◽  
Universal Form ◽  
Definition Of ◽  
Taxonomic Trees

In this chapter, a concept of using incomplete or fuzzy ontologies in decision making is presented. A definition of ontology and of ontological models is given, as well as their formal representation by taxonomic trees, bi-partite graphs, multigraphs, relations, super-relations and hyper-relations. The definitions of the corresponding mathematical notions are also given. Then, the concept of ontologies representing incomplete or uncertain domain knowledge is presented. This concept is illustrated by an example of decision making in medicine. The aim of this chapter is to give an outlook on the possibility of ontological models extension in order to use them as an effective and universal form of domain knowledge representation in computer systems supporting decision making in various application areas.

Smartphone-Based Indoor Visual Navigation with Leader-Follower Mode

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Jingao Xu ◽  
Erqun Dong ◽  
Qiang Ma ◽  
Chenshu Wu ◽  
Zheng Yang
Keyword(s):  
Real Time ◽  
Environmental Changes ◽  
State Of The Art ◽  
Visual Navigation ◽  
Indoor Navigation ◽  
Location Services ◽  
Localization And Mapping ◽  
Leaders And Followers ◽  
Indoor Navigation System ◽  
Free Pair

Existing indoor navigation solutions usually require pre-deployed comprehensive location services with precise indoor maps and, more importantly, all rely on dedicatedly installed or existing infrastructure. In this article, we present Pair-Navi, an infrastructure-free indoor navigation system that circumvents all these requirements by reusing a previous traveler’s (i.e., leader) trace experience to navigate future users (i.e., followers) in a Peer-to-Peer mode. Our system leverages the advances of visual simultaneous localization and mapping ( SLAM ) on commercial smartphones. Visual SLAM systems, however, are vulnerable to environmental dynamics in the precision and robustness and involve intensive computation that prohibits real-time applications. To combat environmental changes, we propose to cull non-rigid contexts and keep only the static and rigid contents in use. To enable real-time navigation on mobiles, we decouple and reorganize the highly coupled SLAM modules for leaders and followers. We implement Pair-Navi on commodity smartphones and validate its performance in three diverse buildings and two standard datasets (TUM and KITTI). Our results show that Pair-Navi achieves an immediate navigation success rate of 98.6%, which maintains as 83.4% even after 2 weeks since the leaders’ traces were collected, outperforming the state-of-the-art solutions by >50%. Being truly infrastructure-free, Pair-Navi sheds lights on practical indoor navigations for mobile users.

Application of a methodological approach to compare ontologies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yudith Cardinale ◽  
Maria Alejandra Cornejo-Lupa ◽  
Alexander Pinto-De la Gala ◽  
Regina Ticona-Herrera
Keyword(s):  
Domain Knowledge ◽  
Design Methodology ◽  
Methodological Approach ◽  
Quality Model ◽  
Specific Domain ◽  
Content Type ◽  
Localization And Mapping ◽  
Golden Standard ◽  
Knowledge Levels ◽  
Practical Implications

Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

scholarly journals Semantic Representation of Domain Knowledge for Professional VR Training

2021 ◽  
pp. 139-150
Author(s):  
Jakub Flotyński ◽  
Paweł Sobociński ◽  
Sergiusz Strykowski ◽  
Dominik Strugała ◽  
Paweł Buń ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Domain Knowledge ◽  
Professional Training ◽  
Semantic Representation ◽  
Essential Element ◽  
Domain Experts ◽  
Training Systems ◽  
Web Standards ◽  
Domain Specific Knowledge ◽  
Vr Training

Domain-specific knowledge representation is an essential element of efficient management of professional training. Formal and powerful knowledge representation for training systems can be built upon the semantic web standards, which enable reasoning and complex queries against the content. Virtual reality training is currently used in multiple domains, in particular, if the activities are potentially dangerous for the trainees or require advanced skills or expensive equipment. However, the available methods and tools for creating VR training systems do not use knowledge representation. Therefore, creation, modification and management of training scenarios is problematic for domain experts without expertise in programming and computer graphics. In this paper, we propose an approach to creating semantic virtual training scenarios, in which users’ activities, mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We have verified the approach by developing a user-friendly editor of VR training scenarios for electrical operators of high-voltage installations.

scholarly journals Profiling NVIDIA Jetson Embedded GPU Devices for Autonomous Machines

2020 ◽  
Author(s):  
Yazhou Li ◽  
Yahong Rosa Zheng
Keyword(s):  
Effective Means ◽  
Field Testing ◽  
Operating Mode ◽  
Autonomous Driving ◽  
Robot Operating System ◽  
Race Car ◽  
Localization And Mapping ◽  
Autonomous Machines ◽  
Particle Filter Algorithm ◽  
Embedded Gpu

This paper presents two methods, tegrastats GUI version jtop and Nsight Systems, to profile NVIDIA Jetson embedded GPU devices on a model race car which is a great platform for prototyping and field testing autonomous driving algorithms. The two profilers analyze the power consumption, CPU/GPU utilization, and the run time of CUDA C threads of Jetson TX2 in five different working modes. The performance differences among the five modes are demonstrated using three example programs: vector add in C and CUDA C, a simple ROS (Robot Operating System) package of the wall follow algorithm in Python, and a complex ROS package of the particle filter algorithm for SLAM (Simultaneous Localization and Mapping). The results show that the tools are effective means for selecting operating mode of the embedded GPU devices.

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

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