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Ontology based autonomous robot task processing framework.

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Bibliographic Details
Title: Ontology based autonomous robot task processing framework.
Authors: Yueguang Ge, Shaolin Zhang, Yinghao Cai, Tao Lu, Haitao Wang, Xiaolong Hui, Shuo Wang
Source: Frontiers in Neurorobotics; 2024, p01-16, 16p
Subject Terms: AUTONOMOUS robots, OBJECT recognition (Computer vision), KNOWLEDGE representation (Information theory), ONTOLOGY, KNOWLEDGE base, ROBOTS
Abstract: Introduction: In recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments. Methods: In this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments. Results: Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework. Discussion: In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference. [ABSTRACT FROM AUTHOR]
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Database: Biomedical Index
Description
Abstract:Introduction: In recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments. Methods: In this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments. Results: Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework. Discussion: In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference. [ABSTRACT FROM AUTHOR]
ISSN:16625218
DOI:10.3389/fnbot.2024.1401075