Social Drone Sharing to Increase UAV Patrolling Autonomy in Pre- and Post-Emergency Scenarios.

Multirotor drones are becoming increasingly popular in a number of application fields, with a unique appeal to the scientific community and the general public. Applications include security, monitoring and surveillance, environmental mapping, and emergency scenario management: in all these areas, two of the main issues to address are the availability of appropriate software architectures to coordinate teams of drones and solutions to cope with the short-term battery life. This article proposes the novel concepts of Social Drone Sharing (SDS) and Social Charging Station (SCS), which provide the basis to address these problems. Specifically, the article focuses on teams of drones in pre- and post-event monitoring and assessment. Using multirotor drones in these situations can be difficult due to the limited flight autonomy when multiple targets need to be inspected. The idea behind the SDS concept is that citizens can volunteer to recharge a drone or replace its batteries if it lands on their property. The computation of paths to inspect multiple targets will then take into account the availability of SCSs to find solutions compatible with the required inspection and flight times. The main contribution of this article is the development of a cloud-based software architecture for SDS mission management, which includes a multi-drone path-optimization algorithm taking the SDS and SCS concepts into account. Experiments in simulation and a lab environment are discussed, paving the path to a larger trial in a real scenario.

Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents.

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.

The CARESSES Randomised Controlled Trial: Exploring the Health-Related Impact of Culturally Competent Artificial Intelligence Embedded Into Socially Assistive Robots and Tested in Older Adult Care Homes.

This trial represents the final stage of the CARESSES project which aimed to develop and evaluate a culturally competent artificial intelligent system embedded into social robots to support older adult wellbeing. A parallel group, single-blind randomised controlled trial was conducted across older adult care homes in England and Japan. Participants randomly allocated to the Experimental Group or Control Group 1 received a Pepper robot for up 18 h across 2 weeks. Two versions of the CARESSES artificial intelligence were tested: a fully culturally competent system (Experimental Group) and a more limited version (Control Group 1). Control Group 2 (Care As Usual) participants did not receive a robot. Quantitative outcomes of interest reported in the current paper were health-related quality of life (SF-36), loneliness (ULS-8), and perceptions of robotic cultural competence (CCATool-Robotics). Thirty-three residents completed all procedures. The difference in SF-36 Emotional Wellbeing scores between Experimental Group and Care As Usual participants over time was significant (F[1] = 6.614, sig = .019, ηp 2 = .258), as was the comparison between Any Robot used and Care As Usual (F[1] = 5.128, sig = .031, ηp 2 = .146). There were no significant changes in SF-36 physical health subscales. ULS-8 loneliness scores slightly improved among Experimental and Control Group 1 participants compared to Care As Usual participants, but this was not significant. This study brings new evidence which cautiously supports the value of culturally competent socially assistive robots in improving the psychological wellbeing of older adults residing in care settings.