Augmented Reality (AR) has been used in various contexts in recent years in order to enhance user experiences in mobile and wearable devices. Various studies have shown the utility of AR, especially in the field of education, where it has been observed that learning results are improved. However, such applications require specialized teams of software developers to create and maintain them. In an attempt to solve this problem and enable educators to easily create AR content for existing textbooks, the ARTutor platform was developed. It consists of a web-based application that acts as an AR authoring tool, and an accompanying mobile application that is used to access and interact with the educational AR content. In addition, the ARTutor application allows students to ask questions verbally and receive answers based on the contents of the book. This means that the system is suitable for distance learning and promotes self-study and independent learning.

Although most species are sensitive to various chemicals, and olfactory skills such as search strategies for finding nutritious substance are seemingly simple, these basic skills are still not fully understood. Traditionally, chemotaxis has been considered as the fundamental chemosensory navigational mechanism for most species. Previous studies have demonstrated, however, that biased random walk is the more fundamental navigational strategy in various types of diffusion fields. Biased random walk is a robust and slow search process, but it has been shown that its efficiency can be enhanced if it is combined with chemotaxis. The present article summarizes previous findings of the authors in olfactory navigation and extends the work to searching in dynamic flow fields, including turbulence. In addition, a cooperative, multi-agent search method has been investigated and shown to be successful in enhancing search efficiency. The significance of these findings is discussed in the context of future plans to implement these strategies in experimental mobile robots.

Augmented Reality (AR) has seen a significant increase in applications in a number of fields in recent years. One of the areas in which AR has been applied to is education, most commonly by means of augmenting educational books. The present paper builds on previous work by using an AR authoring environment and a mobile application, developed by the authors, in undergraduate courses at the Eastern Macedonia & Thrace Institute of Technology. Using the authoring tool, the students were able to enhance existing secondary education textbooks by adding digital content to them, and, using the mobile application, view the digital content and retrieve information from the textual content of the books by asking questions in natural language form. At the end of the semester, the students were asked to evaluate the ARTutor platform by means of questionnaires and the SECTIONS framework. This study presents and discusses the results of this evaluation exercise and proposes new directions of the research.

Although chemical sensing is far simpler than vision or hearing, navigation in a chemical diffusion field is still not well understood. Biological studies have already demonstrated the use of various search methods (e.g., chemotaxis and biased random walk), but robotics research could provide new ways to investigate principles of olfactory-based search skills (Webb, 2000;Grasso, 2001). In previous studies on odour source localisation, we have tested three biologically inspired search strategies: chemotaxis, biased random walk, and a combination of these methods (Kadar and Virk, 1998;Lytridis et al., 2001). The main objective of the present paper is to demonstrate how simulation and robot experiments could be used conjointly to systematically study these search strategies. Specifically, simulation studies are used to calibrate and test our three strategies in concentric diffusion fields with various noise levels. An experiment with a mobile robot was also conducted to assess these strategies in a real diffusion field. The results of this experiment are similar to those of simulation studies showing that chemotaxis is a more efficient but less robust strategy than biased random walk. Overall, the combined strategy seems to be superior to chemotaxis and biased random walk in both simulation and robot experiment.

Agricultural robotics has been a popular subject in recent years from an academic as well as a commercial point of view. This is because agricultural robotics addresses critical issues such as seasonal shortages in manual labor, e.g., during harvest, as well as the increasing concern regarding environmentally friendly practices. On one hand, several individual agricultural robots have already been developed for specific tasks (e.g., for monitoring, spraying, harvesting, transport, etc.) with varying degrees of effectiveness. On the other hand, the use of cooperative teams of agricultural robots in farming tasks is not as widespread; yet, it is an emerging trend. This paper presents a comprehensive overview of the work carried out so far in the area of cooperative agricultural robotics and identifies the state-of-the-art. This paper also outlines challenges to be addressed in fully automating agricultural production; the latter is promising for sustaining an increasingly vast human population, especially in cases of pandemics such as the recent COVID-19 pandemic.