Human–robot collaboration is a key factor for the development of factories of the future, a space in which humans and robots can work and carry out tasks together. Safety is one of the most critical aspects in this collaborative human–robot paradigm. This article describes the experiments done and results achieved by the authors in the context of the FourByThree project, aiming to measure the trust of workers on fenceless human–robot collaboration in industrial robotic applications as well as to gauge the acceptance of different interaction mechanisms between robots and human beings.

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.

Identity verification and proctoring of online students are one of the key challenges to online learning today. Especially for online certification and accreditation, the training organizations need to verify that the online students who completed the learning process and received the academic credits are those who registered for the courses. Furthermore, they need to ensure that these students complete all the activities of online training without cheating or inappropriate behaviours. The COVID-19 pandemic has accelerated (abruptly in certain cases) the migration and implementation of online education strategies and consequently the need for safe mechanisms to authenticate and proctor online students. Nowadays, there are several technologies with different grades of automation. In this paper, we deeply describe a specific solution based on the authentication of different biometric technologies and an automatic proctoring system (system workflow as well as AI algorithms), which incorporates features to solve the main concerns in the market: highly scalable, automatic, affordable, with few hardware and software requirements for the user, reliable and passive for the student. Finally, the technological performance test of the large scale system, the usability-privacy perception survey of the user and their results are discussed in this work.

This article presents a semantic approach for multimodal interaction between humans and industrial robots to enhance the dependability and naturalness of the collaboration between them in real industrial settings. The fusion of several interaction mechanisms is particularly relevant in industrial applications in which adverse environmental conditions might affect the performance of vision-based interaction (e.g. poor or changing lighting) or voice-based interaction (e.g. environmental noise). Our approach relies on the recognition of speech and gestures for the processing of requests, dealing with information that can potentially be contradictory or complementary. For disambiguation, it uses semantic technologies that describe the robot characteristics and capabilities as well as the context of the scenario. Although the proposed approach is generic and applicable in different scenarios, this article explains in detail how it has been implemented in two real industrial cases in which a robot and a worker collaborate in assembly and deburring operations.