Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed o...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/9/3219 |
| _version_ | 1797568778574233600 |
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| author | Sung-Hyeon Joo Sumaira Manzoor Yuri Goncalves Rocha Sang-Hyeon Bae Kwang-Hee Lee Tae-Yong Kuc Minsung Kim |
| author_facet | Sung-Hyeon Joo Sumaira Manzoor Yuri Goncalves Rocha Sang-Hyeon Bae Kwang-Hee Lee Tae-Yong Kuc Minsung Kim |
| author_sort | Sung-Hyeon Joo |
| collection | DOAJ |
| description | Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework. |
| first_indexed | 2024-03-10T20:01:56Z |
| format | Article |
| id | doaj.art-e3623ecb51c84605a61ddb507a8a0800 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:01:56Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e3623ecb51c84605a61ddb507a8a08002023-11-19T23:33:18ZengMDPI AGApplied Sciences2076-34172020-05-01109321910.3390/app10093219Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment ModelingSung-Hyeon Joo0Sumaira Manzoor1Yuri Goncalves Rocha2Sang-Hyeon Bae3Kwang-Hee Lee4Tae-Yong Kuc5Minsung Kim6Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, KoreaRobot R&D Group, Korea Institute of Industrial Technology (KITECH), Ansan 15588, KoreaDepartment of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electronic and Electrical Engineering, Dongguk University-Seoul Campus, Seoul 04620, KoreaHumans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.https://www.mdpi.com/2076-3417/10/9/3219intelligent robotautonomous navigation frameworktriplet ontological semantic modelenvironment modelingon-demand databaseknowledge-based recognition |
| spellingShingle | Sung-Hyeon Joo Sumaira Manzoor Yuri Goncalves Rocha Sang-Hyeon Bae Kwang-Hee Lee Tae-Yong Kuc Minsung Kim Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling Applied Sciences intelligent robot autonomous navigation framework triplet ontological semantic model environment modeling on-demand database knowledge-based recognition |
| title | Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling |
| title_full | Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling |
| title_fullStr | Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling |
| title_full_unstemmed | Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling |
| title_short | Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling |
| title_sort | autonomous navigation framework for intelligent robots based on a semantic environment modeling |
| topic | intelligent robot autonomous navigation framework triplet ontological semantic model environment modeling on-demand database knowledge-based recognition |
| url | https://www.mdpi.com/2076-3417/10/9/3219 |
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