Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
Source abstract
Abstract
Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Automated Systems · Modular Robots and Swarm Intelligence · Cognitive Computing and Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand
School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
References (39)
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