Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling

Abstract

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.

A one decade survey of autonomous mobile robot systems

“…In addition, navigating a moving robot requires additional skills, including control capabilities, trajectory planning, obstacle avoidance, and safe distances to the goal, which mobile robots need to perform to provide superior navigation. Every one of the navigation systems must consider the general design mentioned above to ensure that all tasks can be met [3], [6], [50]. Over the past two decades, researchers have studied the problems, methods, and applications of mobile robot's navigation systems.…”

“…Over the past two decades, researchers have studied the problems, methods, and applications of mobile robot's navigation systems. Mobile robot navigation has been taken under consideration as one of the major areas of application that is gaining a lot of attention due to its wide range of potential applications [2], [3], [50]. In addition, there are many works of research in which people have tried to solve the navigation issue and examples of their successful applications.…”

“…Also, it requires a control system or cognition unit for coordinating all sub-systems which include robotics [1], indicating that robots should be capable of navigating with no disruptions and capably of avoiding obstacles in the confinement of movement in outdoor or indoor environments [2]. In addition, environment modeling, recognition, planning as well as motion have been a vital component for intelligent systems, allowing them for understanding and perceiving a complex environment similar to the way humans do and reliably performing tasks [3].…”

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

“…In Table 1, a black circle ( ) means that the corresponding ontology conceptualizes the respective subcategory; a gray circle ( ) represents that the on-tology partially models the corresponding subcategory; and an empty circle ( ) designates ontologies that do not conceptualize the subcategory. SUMO, 2007 [18] Space Ontology, 2010 [8] OUR-K, 2011 [19] PROTEUS, 2011 [20] Uncertain Ontology, 2011 [21] Wang and Chen, 2011 [22] KnowRob, 2012 [13] Hotz et al, 2012 [23] OASys, 2012 [24] Core Ontology, 2013 [10] Li et al, 2013 [25] POS, 2013 [26] V. Fortes, 2013 [12] Wu et al, 2014 [27] RoboEarth, 2015 [28] ROSPlan, 2015 [9] Burroughes and Gao, 2017 [29] ADROn, 2018 [30] Deeken et al, 2018 [31] Sun et al, 2019 [32] ISRO, 2020 [11] Crespo et al, 2020 [33] Sung-Hyeon et al, 2020 [34] BIRS, 2021 [35] Shchekotov et al, 2021 [36] OntoSLAM Almost all analyzed ontologies represent partial knowledge of Robot Information, only PROTEUS [20] covers all subcategories. Robot Ontology [15], SUMO [18], ADROn [30], and OASys [24] only model partial knowledge for this category, neglecting all other categories.…”

“…Few of the revised ontologies partially model the knowledge of Timely Information [11,12,17,19,28,29,34,36], also these analyzed ontologies partially model aspects in all categories.…”