Ontology-Based Framework for Cooperative Learning of 3D Object Recognition

Parkpoom Chaisiriprasert,Matthew N Dailey,Karn Yongsiriwit,Chutiporn Anutariya

doi:10.3390/app11178080

Parkpoom Chaisiriprasert, Matthew N Dailey + Show 2 more

Open Access

https://doi.org/10.3390/app11178080

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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.

Highlights

Introduction iationsService robots and automation systems have rapidly evolved to support humans’day-to-day activities in the home, office, and other places [1]
To demonstrate common understanding of objects learned by the system, we developed a web application integrating the Robotic Object Description (ROD) ontology and the WordNet API for semantic analysis
We have presented an ontology-based framework for cooperative robot learning that utilizes knowledge representations in the Web Ontology Language

Summary

Introduction

Introduction iationsService robots and automation systems have rapidly evolved to support humans’day-to-day activities in the home, office, and other places [1]. Service robots and automation systems have rapidly evolved to support humans’. Among the many possible features of a service robot, one of the most important for effective human assistance is object recognition [2]. Effective 3D object recognition requires a large amount of storage and power consumption compared to typical robot systems. Offloading necessary data storage and computing from service robots to a centralized infrastructure would reduce robots’ power consumption and reduce the complexity of their firmware. This idea has led us and other researchers to explore the use of cloud computing to support service robots, especially in the use case of 3D object recognition [3]. By providing a means for knowledge sharing, can enable multiple service robots to cooperate in learning

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Conclusion