The supervision of food production systems is instrumental in the advancement of food security. Remote access and control provides unique capabilities to the supervision and operation of such systems, as well as interesting opportunities for students to access learning factory facilities remotely. Thus, the AllFactory at the University of Alberta provides a unique environment that allows for testing of highly robotized food production lines. This paper proposes the use of digital twin models to enable remote access to learning factory’s systems and combines distributed sensors and computer vision to visualize the systems' operational status and motions while also providing a remote learning environment. In this study, a digital twin of a robotic seed sowing system, consisting of a Dobot M1 robotic arms is developed and tested. The robot system aims to pick crop seeds using pneumatic actuators and finally place them correctly in rockwool, while several cameras monitor seed and plant growth. The development of this tool hopes to support the continuous use of learning factories even in complicated situations.
|Number of pages
|SSRN Electronic Journal
|Published - 22 Apr 2022
|12th Conference on Learning Factories 2022 (CLF) - Singapore, Singapore
Duration: 11 Apr 2022 → 13 Apr 2022