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.

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.

In the viticulture sector, robots are being employed more frequently to increase productivity and accuracy in operations such as vineyard mapping, pruning, and harvesting, especially in locations where human labor is in short supply or expensive. This paper presents the development of an algorithm for grape maturity estimation in the framework of vineyard management. An object detection algorithm is proposed based on You Only Look Once (YOLO) v7 and its extensions in order to detect grape maturity in a white variety of grape (Assyrtiko grape variety). The proposed algorithm was trained using images received over a period of six weeks from grapevines in Drama, Greece. Tests on high-quality images have demonstrated that the detection of five grape maturity stages is possible. Furthermore, the proposed approach has been compared against alternative object detection algorithms. The results showed that YOLO v7 outperforms other architectures both in precision and accuracy. This work paves the way for the development of an autonomous robot for grapevine management.

Weeds are one of the primary concerns in agriculture since they compete with crops for nutrients and water, and they also attract insects and pests and are, therefore, hindering crop yield. Moreover, seasonal labour shortages necessitate the automation of such agricultural tasks using machines. For this reason, advances in agricultural robotics have led to many attempts to produce autonomous machines that aim to address the task of weeding both effectively and efficiently. Some of these machines are implementing chemical-based weeding methods using herbicides. The challenge for these machines is the targeted delivery of the herbicide so that the environmental impact of the chemical is minimised. However, environmental concerns drive weeding robots away from herbicide use and increasingly utilise mechanical weeding tools or even laser-based devices. In this case, the challenge is the development and application of effective tools. This paper reviews the progress made in the field of weeding robots during the last decade. Trends during this period are identified, and the current state-of-the-art works are highlighted. Finally, the paper examines the areas where the current technological solutions are still lacking, and recommendations on future directions are made.

Robots are more and more present in our lives, particularly in the health sector. In therapeutic centers, some therapists are beginning to explore various tools like video games, Internet exchanges, and robot-assisted therapy. These tools will be at the disposal of these professionals as additional resources that can support them to assist their patients intuitively and remotely. The humanoid robot can capture young children’s attention and then attract the attention of researchers. It can be considered as a play partner and can directly interact with children or without a third party’s presence. It can equally perform repetitive tasks that humans cannot achieve in the same way. Moreover, humanoid robots can assist a therapist by allowing him to teleoperated and interact from a distance. In this context, our research focuses on robot-assisted therapy and introduces a humanoid social robot in a pediatric hospital care unit. That will be performed by analyzing many aspects of the child’s behavior, such as verbal interactions, gestures and facial expressions, etc. Consequently, the robot can reproduce consistent experiences and actions for children with communication capacity restrictions. This work is done by applying a novel approach based on deep learning and reinforcement learning algorithms supported by an ontological knowledge base that contains relevant information and knowledge about patients, screening tests, and therapies. In this study, we realized a humanoid robot that will assist a therapist by equipping the robot NAO: 1) to detect whether a child is autistic or not using a convolutional neural network, 2) to recommend a set of therapies based on a selection algorithm using a correspondence matrix between screening test and therapies, and 2) to assist and monitor autistic children by executing tasks that require those therapies.