A Realistic Simulation for Swarm UAVs and Performance Metrics for Operator User Interfaces

Robots have been utilized to support disaster mitigation missions through exploration of areas that are either unreachable or hazardous for human rescuers [1]. The great potential for robotics in disaster mitigation has been recognized by the research community and during the last decade, a lot of research has been focused on developing robotic systems for this purpose. In this thesis, we present a description of the usage and classification of UAVs and performance metrics that affect controlling of UAVs. We also present new contributions to the UAV simulator developed by ECSL and RRL: the integration of flight dynamics of Hummingbird quadcopter, and distance optimization using a Genetic algorithm

Magician simulator — A realistic simulator for heterogeneous teams of autonomous robots

We report on the development of a new simulation environment for use in Multi-Robot Learning, Swarm Robotics, Robot Teaming, Human Factors and Operator Training. The simulator provides a realistic environment for examining methods for localization and navigation, sensor analysis, object identification and tracking, as well as strategy development, interface refinement and operator training (based on various degrees of heterogeneity, robot teaming, and connectivity). The simulation additionally incorporates real-time human-robot interaction and allows hybrid operation with a mix of simulated and real robots and sensor inputs

Applying human-centered AI in developing effective human-AI teaming: A perspective of human-AI joint cognitive systems

Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. HAT recognizes that AI will function as a teammate instead of simply a tool in collaboration with humans. Effective human-AI teams need to be capable of taking advantage of the unique abilities of both humans and AI while overcoming the known challenges and limitations of each member, augmenting human capabilities, and raising joint performance beyond that of either entity. The National AI Research and Strategic Plan 2023 update has recognized that research programs focusing primarily on the independent performance of AI systems generally fail to consider the functionality that AI must provide within the context of dynamic, adaptive, and collaborative teams and calls for further research on human-AI teaming and collaboration. However, there has been debate about whether AI can work as a teammate with humans. The primary concern is that adopting the "teaming" paradigm contradicts the human-centered AI (HCAI) approach, resulting in humans losing control of AI systems. This article further analyzes the HAT paradigm and the debates. Specifically, we elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) and apply it to represent HAT under the HCAI umbrella. We believe that HAIJCS may help adopt HAI while enabling HCAI. The implications and future work for HAIJCS are also discussed. Insights: AI has led to the emergence of a new form of human-machine relationship: human-AI teaming (HAT), a paradigmatic shift in human-AI systems; We must follow a human-centered AI (HCAI) approach when applying HAT as a new design paradigm; We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT for developing effective human-AI teamingComment:

An Evaluation Schema for the Ethical Use of Autonomous Robotic Systems in Security Applications

We propose a multi-step evaluation schema designed to help procurement agencies and others to examine the ethical dimensions of autonomous systems to be applied in the security sector, including autonomous weapons systems

Spacecraft Dormancy Autonomy Analysis for a Crewed Martian Mission

Current concepts of operations for human exploration of Mars center on the staged deployment of spacecraft, logistics, and crew. Though most studies focus on the needs for human occupation of the spacecraft and habitats, these resources will spend most of their lifetime unoccupied. As such, it is important to identify the operational state of the unoccupied spacecraft or habitat, as well as to design the systems to enable the appropriate level of autonomy. Key goals for this study include providing a realistic assessment of what "dormancy" entails for human spacecraft, exploring gaps in state-of-the-art for autonomy in human spacecraft design, providing recommendations for investments in autonomous systems technology development, and developing architectural requirements for spacecraft that must be autonomous during dormant operations. The mission that was chosen is based on a crewed mission to Mars. In particular, this study focuses on the time that the spacecraft that carried humans to Mars spends dormant in Martian orbit while the crew carries out a surface mission. Communications constraints are assumed to be severe, with limited bandwidth and limited ability to send commands and receive telemetry. The assumptions made as part of this mission have close parallels with mission scenarios envisioned for dormant cis-lunar habitats that are stepping-stones to Mars missions. As such, the data in this report is expected to be broadly applicable to all dormant deep space human spacecraft

Ethical governance is essential to building trust in robotics and artificial intelligence systems

© 2018 The Author(s) Published by the Royal Society. All rights reserved. This paper explores the question of ethical governance for robotics and artificial intelligence (AI) systems. We outline a roadmap-which links a number of elements, including ethics, standards, regulation, responsible research and innovation, and public engagement-as a framework to guide ethical governance in robotics and AI. We argue that ethical governance is essential to building public trust in robotics and AI, and conclude by proposing five pillars of good ethical governance. This article is part of the theme issue 'Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio