This work illustrates the testing of the Spot Robot performed at the training camp of Civil Protection and ANPAS (National Association of Public Assistance) in Foligno. The camp simulates the aftermath of an earthquake with different types of collapsed buildings. We teleoperated the quadruped Spot robot in different areas of the camp where Spot needs to address different challenges. The focus of the testing was not on the objective performance of the robot but on how the robot was subjectively perceived by rescuers of ANPAS and Civil Protection. Initially, we formulated and tested two hypotheses to check if locomotion in some areas is perceived better than in other areas and if there are perceivable differences when the robot is using different types of locomotion gaits. Then, we conducted unstructured interviews with participants who observed the robot in action to describe their rescue procedures and give us suggestions and opinions on what operations they expect the robot might perform.

This study introduces a system implemented on a legged robot, designed to generate a multi-layered map that incorporates semantic information, specifically tailored for Search & Rescue robotics. The article discusses the development of a Machine Learning model based on visual data for recognizing people and environmental features, and its integration into a mapping and navigation architecture. The system was tested in two different locations using the Spot robot by Boston Dynamics, equipped with an external ZED2 depth camera. Tests are described in detail and results analyzed.

This work describes an immersive control system for a quadruped robot, designed to track the head movements of the operator wearing a virtual reality eye-wear, while also utilizing joystick commands for locomotion control. The article details the implemented closed-loop velocity control approach and the locomotion task specifications. The proposed method has been implemented on a Spot robot from Boston Dynamics, with Meta Quest 2 virtual reality system. Evaluation of the approach involved a user study, where participants engaged in immersive control of the quadruped robot within an indoor experimental environment and provided feedback through standardized questionnaires. Pairwise comparison of the resulting data revealed significant advantages for the proposed immersive control system over a standard remote controller, with enhanced performance observed in the second trial of using the control system. However, participants lacking experience with virtual reality systems reported increased distress symptoms following the experiment.Code: https://www.github.com/aliy98/zed-oculus-spot

In this work, we propose a strategy to implement the first two steps of the DRABC paradigm (Danger, Response, Airway, Breathing, Circulation) used by rescuers in Search and Rescue (SAR) with the use of a mobile quadruped robot. The robot is programmed to autonomously explore and create a map of the environment with the main objective of identifying areas of danger and reporting them to rescuers (first step of DRABC). While completing this first goal the robot must also identify people still inside the building, mark their position but also evaluate the health state of the person and in particular the response (second step of DRABC). Specifically, we propose new strategies for SAR considering that autonomous behaviour is particularly relevant before the human rescuers arrive: therefore, the policy adopted should privilege covering a broader area in the available time, rather than exploring a smaller area in depth. Strategies have been tested with the Spot robot from Boston Dynamics concerning both exploration and health assessment. The software developed and the tests to validate it are thoroughly described and explained.