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In the last decades, the focus has shifted towards mobile robots to link predictions, imagination, and expectations to human life in different aspects. A tremendous amount of research on mobile robots indicates their importance in various industrial and non-industrial fields such as production, medicine and agriculture. Despite all of these innovations in the field of robotics, intelligent mobile robots are facing challenges in processing the vast amount of sensory data from their sensory inputs. Due to the increasing amount of sensory data, a new demand is to process sensory inputs in an understandable form for both humans and robots. One approach to processing sensory data in a way that is understandable for both robots and humans is through the use of semantic technology, which is a major technology for building semantic knowledge bases in a machine-readable form. The success of semantic technology is highly reliant on ontologies which are considered the semantic knowledge representation. The huge amount of research in this field proves the undeniable impact of ontologies in the field of robotics. Yet, the work concerning the conversion of the sensory inputs from a mobile robot into semantic information in real-time are scarce. This transformation becomes more challenging when converting the sensory input of multiple sensors to a single semantic statement. The collection of semantic information for real-time ontology population is another challenge. In this regard, there is also a lack of work in transferring and using this information for natural language communication between humans and robots. This work addresses these challenges and employs semantic technology in the field of robotics to enable a mobile robot to create semantic information during exploration. In addition, the resulted semantic information is used for communicating information as well as facilitating communication between other robots and humans in a natural language.

The leaf, as the vital part of plants, can be affected by physical and physiological factors which might lead to changes in its shape, color and size. These unique parts play an essential role in the design and implementation of plant recognition systems, as the shapes of leaves vary among different plants. Weather type and related factors, such as light intensity, humidity, temperature and wind-speed, may have effects on the number of leaves that grow on a plant, the amount of fresh and dried leaves, the deformation of leaves, color of leaves, positions of leaves on branches, etc. In addition, photographing in outdoor environments with different weather conditions has undesired impacts on images. For instance, light scattering changes severely when there are water droplets during photographing which influences the images captured in rainy weather. Despite the importance of the proposed factors and the relevant effects on the plants, leaves and images taken from plants, a plant recognition system should be independent from all environmental and non-environmental factors to be practically useful and applicable for identifying plant species in uncontrolled outdoor environments. Moreover, changes in the time of day (morning, noon and evening), the distance between camera and plant species as well as the angle and illumination of the object and environment when the images are taken should be considered in the process of plant recognition. For instance, in a windy weather condition, for an observer even from a short distance, it is challenging to distinguish all single leaves and identify the plant type from the shape of its leaf. Furthermore, the unstructured background of the images is another challenge which affects the images captured in fields and outdoor environments. These are only some difficulties for identification of plants in natural environments such as farms, forests, etc. A consideration of mentioned factors contributes to developing a novel, efficient and

Modernization of living environments and human activities have severe effects on many parameters and factors such as climate change and global warming, an increase of incidence and the severity of wildfires, land surfaces and ice sheets, ecological imbalance, change of fertility of the soil, flow of energy, food security, etc. In addition, human modernization has had a negative impact on biodiversity and the natural environment. An integral component of modernization is agriculture that associates with the outdoor environment and relevant issues. Automation of agricultural activities contributes to reducing the dependency on human labor and the harmful effects on the natural environment. The correct identification of plants in outdoor environments has been neglected and many critical environmental and non-environmental limits and factors, such as weather conditions, time, viewpoint, lighting conditions (illuminations and light intensity), distance, etc., have not been considered in existing plant recognition systems and technologies. Hence, there is a demand to develop mobile and real-time systems for plant recognition in natural environments. This paper addresses these challenges and introduces the application of autonomous mobile robot and semi-robots for recognition of natural plant species in outdoor environments. Furthermore, the proposed system presents the use of employing low-cost cameras, such as iPhone 6s, Canon EOS 600D and Samsung Galaxy Note 4, for plant recognition system in real-time. The performance of the system has been tested with a number of experiments in different years, 2017 and 2018, and at different times of day, morning and evening. The proposed system is a combination of new technologies involving deep learning concepts and an autonomous field robot to carry out precise plant recognition in challenging environments. The final accuracy of the mobile real-time system is 84.1666%.

Today, thanks to the advancement of robotics and intelligent new technologies, robots are entering more and more into different areas of human life. In doing so, the interaction between humans and robots, involving solving tasks both supervised and collaborative, becomes increasingly important as they both rely on smart approaches for communication. Throughout the human-robot and robot-robot communication, the utilized technology has to be able to describe information that is semantically rich, structurally efficient concerning description and time efficient regarding the retrieval of information. This work utilizes the resource description framework (RDF), as one of the core technologies of the semantic web to describe the navigation of a mobile robot semantically in an ontology which is populated in real-time. This research proposes a new and efficient way of expressing semantic relationships within the navigation ontology by using helper nodes. In contrast to existing approaches, it could be shown that the use of helper nodes can cut down the generation of nodes by a factor of two, improving the storage but also the utilization for a time efficient communication.

Fault Detection, Isolation and Recovery (FDIR) are becoming increasingly indispensable for real-time systems. Active diagnosis systems, therefore, are endeavoring to increase the reliability of real-time systems by computing at run-time the diagnostic information within FDIR systems, to actively decide on recovery actions. For embedded systems that contain various components, especially those including sensors and actuators, it is essential to provide a platform in which these components are able to communicate and also serve for an active diagnosis system. This paper proposes the ADISTES ontology (Active Diagnosis based on Semantic Web Technologies for Distributed Embedded Real-Time Systems) for an active diagnosis system of sensors and actuators. The proposed ADISTES ontology is a general ontology that can be used by different cases and systems to integrate their sensors and actuators; it can be instantiated from heterogeneous sources. As a characteristic of ontologies, they provide information in a machine interpretable format. Hence, the ADISTES ontology can also play a role for different sensors and actuators to share and communicate their status with one another.

Today, thanks to the advancement of robotics and the achievements that are made in the light of the development of automated and intelligent technologies, we are increasingly witnessing the arrival of robotic devices and machines into different areas of human life. With all of the achievements and their intelligence, the degree of their proficiency relies in many cases on their ability to interact with human beings. To do so, for a mobile robot the ability to understand its surroundings and also to describe it in a way that human can easily understand is vitally important. “Above”, “below”, “in front of”, “behind of”, “left of” and “right to” are prepositions that human uses in daily life to describe not only the relationship between objects but also to provide one's location implicitly. The Resource Description Framework, RDF, as one of the semantic web core components, can be employed for forming those prepositions. The idea is to utilize a smart sensor of a simulated, mobile robot for creating semantic information in RDF format that represents inter-object relations by using the same spatial prepositions that human uses in his daily life to describe his environment.

By the increase of automation, many questions arise about the future of robots in human life, how a robot can communicate with another robot, how it can communicate with a human, and how it can interact with objects of its surrounding-these are some of the critical questions to be answered. For the robot to be able to answer questions and intelligently interact with its environment, it is essential to have a proper understanding of its surrounding; therefore, information must be represented in a way in which a robot can understand them. Semantic technology provides some tools and techniques for expressing information that is understandable by both human and robots. This work presents a novel approach of using Resource Description Framework, RDF, which is a semantic web tool for representing information semantically in the robotics field. For this purpose, a simulated mobile robot equipped with a simulated camera sensor is located in a simulated outdoor environment for navigation. The output of the simulated sensor is collected and converted into a set of statements in RDF format; the obtained statements provide meaning to the detected objects in a way that a robot can understand and utilize.

An artificial compound eye system is the bionic system of natural compound eyes with much wider field-of-view, better capacity to detect moving objects and higher sensitivity to light intensity than ordinary single-aperture eyes. In recent years, renewed attention has been paid to the artificial compound eyes, due to their better characteristics inheriting from insect compound eyes than ordinary optical imaging systems. This paper provides a comprehensive survey of the state-of-the-art work on artificial compound eyes. This review starts from natural compound eyes to artificial compound eyes including their system design, theoretical development and applications. The survey of artificial compound eyes is developed in terms of two main types: planar and curved artificial compound eyes. Finally, the most promising future research developments are highlighted.