ABSTRACT
Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human-robot collaboration (HRC) in industrial automation.
Subject(s)
Automation , Industry , Robotics , Humans , IntentionABSTRACT
Two experiments were done in 2008 and 2009 to study the effects of magnetic field and silver nanoparticles on fodder maize (Zea mays L.). These experiments were done with seven treatments based on a randomized complete block design in four replications. The treatments were as follows: magnetic field and silver nanoparticles + Kemira fertilizer (T1), magnetic field and silver nanoparticles + Humax fertilizer (T2), magnetic field and silver nanoparticles (T3), Kemira fertilizer (T4), Librel fertilizer (T5), Humax fertilizer (T6), and a control (T7). Results showed that fresh yield was higher in treatments T3 and T4. Treatments T3 and T4 had increased maize fresh yields of 35 and 17.5 % in comparison to the control, respectively. The dry matter yield of those plants exposed to magnetic field and silver nanoparticles was significantly higher than that from any of the other treatments. Magnetic field and silver nanoparticle treatments (T3 and T1) showed higher percentages for ears, and the lowest percentages were found in treatments T7 and T5. In general, the soil conditions for crop growth were more favorable in 2009 than in 2008, which caused the maize to respond better to treatments tested in the study; therefore, treatments had more significant effects on studied traits in 2008 than in 2009.
