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The Internet of Things paradigm envisions the interoperation among objects, people, and their surrounding environment. In the last decade, the spread of IoT-based solutions has been supported in various domains and scenarios by academia, industry, and standards-setting organizations. The wide variety of applications and the need for a higher level of autonomy and interaction with the environment have recently led to the rise of the Internet of Robotic Things (IoRT), where smart objects become autonomous robotic systems. As mentioned in the recent literature, many of the proposed solutions in the IoT field have to tackle similar challenges regarding the management of resources, interoperation among objects, and interaction with users and the environment. Given that, the concept of the IoT pattern has recently been introduced. In software engineering, a pattern is defined as a general solution that can be applied to a class of common problems. It is a template suggesting a solution for the same problem occurring in different contexts. Similarly, an IoT pattern provides a guide to design an IoT solution with the difference that the software is not the only element involved. Starting from this idea, we propose the novel concept of the IoRT pattern. To the authors’ knowledge, this is the first attempt at pattern authoring in the Internet of Robotic Things context. We focus on pattern identification by abstracting examples also in the Ambient Assisted Living (AAL) scenario. A case study providing an implementation of the proposed patterns in the AAL context is also presented and discussed.

The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. Several well-established communities in robotics research deal with these scenarios such as search and rescue robotics, planetary exploration, and agricultural robotics. Perception plays a crucial role in this context, since it provides the necessary information to make the vehicle aware of its own status and its surrounding environment. We present a review on the recent contributions in the robotics literature adopting learning-based methods to solve the problem of environment perception and interpretation with the final aim of the autonomous context-aware navigation of ground vehicles in unstructured environments. To the best of our knowledge, this is the first work providing such a review in this context.

Surveying with unmanned aerial vehicles flying close to the terrain is crucial for the collection of details that are not visible when flying at higher altitudes. This type of missions can be applied in several scenarios such as search and rescue, precision agriculture, and environmental monitoring, to name a few. We present a strategy for the generation of low-altitude trajectories for terrain following. The trajectory is generated taking into account the morphology of the area of interest, represented as a georeferenced Digital Surface Model (DSM), while ensuring a safe separation from any obstacle. The surface model of the scenario is created by using a UAV-based photogrammetry software, which processes the images acquired during a preliminary mission at high altitude. The solution was developed, tested, and verified both in simulation and in real scenarios with a multirotor equipped with low-cost sensing. The experimental results proved the validity of the generation of trajectories at altitudes lower than most of the works available in the literature. The images acquired during the low-altitude mission were processed to obtain a high-resolution reconstruction of the area as a representative application result.