Because photovoltaic (PV) plants require periodic maintenance, using unmanned aerial vehicles (UAV) for inspections can help reduce costs. Usually, the thermal and visual inspection of PV installations works as follows. A UAV equipped with a global positioning system (GPS) receiver is assigned a flight zone, which the UAV will cover back and forth to collect images to be subsequently composed in an orthomosaic. When doing this, the UAV typically flies at a height above the ground that is appropriate to ensure that images overlap even in the presence of GPS positioning errors. However, this approach has two limitations. First, it requires covering the whole flight zone, including “empty” areas between PV module rows. Second, flying high above the ground limits the resolution of the images to be subsequently inspected. The article proposes a novel approach using an autonomous UAV with an RGB and a thermal camera for PV module tracking through segmentation and visual servoing, which does not require a GPS except for measuring the “small” relative displacement between a PV module row and the next one. With this solution, the UAV moves along PV module rows at a lower height than usual and inspects them back and forth in a boustrophedon way by ignoring “empty” areas with no PV modules. Experimental tests performed in simulation and at an actual PV plant are reported, showing a tracking error lower than 0.2 m in most situations when moving at 1.2 m/s.

The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.