Ontology based autonomous robot task processing framework

• Published in: Frontiers in neurorobotics Vol. 18; p. 1401075
• Main Authors: Ge, Yueguang, Zhang, Shaolin, Cai, Yinghao, Lu, Tao, Wang, Haitao, Hui, Xiaolong, Wang, Shuo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 07.05.2024; Frontiers Media S.A
• Subjects: Adaptability; Cognition & reasoning; Cognitive ability; Control algorithms; Deep learning; Design; Knowledge management; Knowledge representation; knowledge-enabled robot; Logic programming; Neural networks; Neuroscience; Ontology; Planning; Semantics; service robot; task planning; Underwater robots; service robot; knowledge representation; ontology; knowledge-enabled robot; task planning
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2024.1401075

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. 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. Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework. In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference.