ICARUS Training and Support System

The ICARUS unmanned tools act as gatherers, which acquire enormous amount of information. The management of all these data requires the careful consideration of an intelligent support system. This chapter discusses the High-Performance Computing (HPC) support tools, which were developed for rapid 3D data extraction, combination, fusion, segmentation, classification and rendering. These support tools were seamlessly connected to a training framework. Indeed, training is a key in the world of search and rescue. Search and rescue workers will never use tools on the field for which they have not been extensively trained beforehand. For this reason, a comprehensive serious gaming training framework was developed, supporting all ICARUS unmanned vehicles in realistic 3D-simulated (based on inputs from the support system) and real environments

Time to understand a cluttered spatial scene.

When searching for people in collapsed buildings, Urban Search and Rescue workers need to comprehend a complex cluttered scene observed through an endoscope under time pressure. This paper addresses the effects of time pressure and spatial ability on the comprehension of a flm showing a mock-up collapsed room that was explored using endoscope-like technology. Participants' task was to find the objects that were hidden in the rubble, describe where they had found them, and draw the scene. Analysis focused on coherence and spatial specificity. Results indicate that spatial skills were most decisive for understanding and conceptually integrating the scene. Time pressure only affected the amount of objects found, not the degree of conceptual integration as re ected in the descriptions

Study of efficiency of USAR operations with assistive technologies

This paper presents presents a study on eciency of Urban Search and Rescue (USAR) missions that has been carried out within the framework of the German research project I-LOV. After three years of development, first field tests have been carried out in 2011 by professionals such as the Rapid Deployment Unit for Salvage Operations Abroad (SEEBA). We present results from evaluating search teams in simulated USAR scenarios equipped with newly developed technical search means and digital data input terminals developed in the I-LOV project. In particular, USAR missions assisted by the “bioradar”, a ground-penetrating radar system for the detection of humanoid movements, a semi-active video probe of more than 10 m length for rubble pile exploration, a snake-like rescue robot, and the decision support system FRIEDAA were evaluated and compared with conventional USAR missions. Results of this evaluation indicate that the developed technologies represent an advantages for USAR missions, which are discussed in this paper. Funding Agencies|German Federal Ministry of Education and Research|13N9759|German Federal Agency for Technical Relief (THW)||RIF e.V.||JT-electronic GmbH||carat robotic innovation GmbH||Berlin- Oberspree Sondermaschinenbau GmbH (BOS)||SEEBA||Artificial Intelligence & Integrated Computer System

Human factors of semi-autonomous robots for urban search and rescue

During major disasters or other emergencies, Urban Search and Rescue (USAR) teams are responsible for extricating casualties safely from collapsed urban structures. The rescue work is dangerous due to possible further collapse, fire, dust or electricity hazards. Sometimes the necessary precautions and checks can last several hours before rescuers are safe to start the search for survivors. Remote controlled rescue robots provide the opportunity to support human rescuers to search the site for trapped casualties while they remain in a safe place. The research reported in this thesis aimed to understand how robot behaviour and interface design can be applied to utilise the benefits of robot autonomy and how to inform future human-robot collaborative systems. The data was analysed in the context of USAR missions when using semi-autonomous remote controlled robot systems. The research focussed on the influence of robot feedback, robot reliability, task complexity, and transparency. The influence of these factors on trust, workload, and performance was examined. The overall goal of the research was to make the life of rescuers safer and enhance their performance to help others in distress. Data obtained from the studies conducted for this thesis showed that semi-autonomous robot reliability is still the most dominant factor influencing trust, workload, and team performance. A robot with explanatory feedback was perceived as more competent, more efficient and less malfunctioning. The explanatory feedback was perceived as a clearer type of communication compared to concise robot feedback. Higher levels of robot transparency were perceived as more trustworthy. However, single items on the trust questionnaire were manipulated and further investigation is necessary. However, neither explanatory feedback from the robot nor robot transparency, increased team performance or mediated workload levels. Task complexity mainly influenced human-robot team performance and the participants’ control allocation strategy. Participants allowed the robot to find more targets and missed more robot errors in the high complexity conditions compared to the low task complexity conditions. Participants found more targets manually in the low complexity tasks. In addition, the research showed that recording the observed robot performance (the performance of the robot that was witnessed by the participant) can help to identify the cause of contradicting results: participants might not have noticed some of the robots mistakes and therefore they were not able to distinguish between the robot reliability levels. Furthermore, the research provided a foundation of knowledge regarding the real world application of USAR in the United Kingdom. This included collecting knowledge via an autoethnographic approach about working processes, command structures, currently used technical equipment, and attitudes of rescuers towards robots. Also, recommendations about robot behaviour and interface design were collected throughout the research. However, recommendations made in the thesis include consideration of the overall outcome (mission performance) and the perceived usefulness of the system in order to support the uptake of the technology in real world applications. In addition, autonomous features might not be appropriate in all USAR applications. When semi-autonomous robot trials were compared to entirely manual operation, only the robot with an average of 97% reliability significantly increased the team performance and reduced the time needed to complete the USAR scenario compared to the manually operated robot. Unfortunately, such high robot success levels do not exist to date. This research has contributed to our understanding of the factors influencing human-robot collaboration in USAR operations, and provided guidance for the next generation of autonomous robots

Human factors of semi-autonomous robots for urban search and rescue

During major disasters or other emergencies, Urban Search and Rescue (USAR) teams are responsible for extricating casualties safely from collapsed urban structures. The rescue work is dangerous due to possible further collapse, fire, dust or electricity hazards. Sometimes the necessary precautions and checks can last several hours before rescuers are safe to start the search for survivors. Remote controlled rescue robots provide the opportunity to support human rescuers to search the site for trapped casualties while they remain in a safe place. The research reported in this thesis aimed to understand how robot behaviour and interface design can be applied to utilise the benefits of robot autonomy and how to inform future human-robot collaborative systems. The data was analysed in the context of USAR missions when using semi-autonomous remote controlled robot systems. The research focussed on the influence of robot feedback, robot reliability, task complexity, and transparency. The influence of these factors on trust, workload, and performance was examined. The overall goal of the research was to make the life of rescuers safer and enhance their performance to help others in distress. Data obtained from the studies conducted for this thesis showed that semi-autonomous robot reliability is still the most dominant factor influencing trust, workload, and team performance. A robot with explanatory feedback was perceived as more competent, more efficient and less malfunctioning. The explanatory feedback was perceived as a clearer type of communication compared to concise robot feedback. Higher levels of robot transparency were perceived as more trustworthy. However, single items on the trust questionnaire were manipulated and further investigation is necessary. However, neither explanatory feedback from the robot nor robot transparency, increased team performance or mediated workload levels. Task complexity mainly influenced human-robot team performance and the participants’ control allocation strategy. Participants allowed the robot to find more targets and missed more robot errors in the high complexity conditions compared to the low task complexity conditions. Participants found more targets manually in the low complexity tasks. In addition, the research showed that recording the observed robot performance (the performance of the robot that was witnessed by the participant) can help to identify the cause of contradicting results: participants might not have noticed some of the robots mistakes and therefore they were not able to distinguish between the robot reliability levels. Furthermore, the research provided a foundation of knowledge regarding the real world application of USAR in the United Kingdom. This included collecting knowledge via an autoethnographic approach about working processes, command structures, currently used technical equipment, and attitudes of rescuers towards robots. Also, recommendations about robot behaviour and interface design were collected throughout the research. However, recommendations made in the thesis include consideration of the overall outcome (mission performance) and the perceived usefulness of the system in order to support the uptake of the technology in real world applications. In addition, autonomous features might not be appropriate in all USAR applications. When semi-autonomous robot trials were compared to entirely manual operation, only the robot with an average of 97% reliability significantly increased the team performance and reduced the time needed to complete the USAR scenario compared to the manually operated robot. Unfortunately, such high robot success levels do not exist to date. This research has contributed to our understanding of the factors influencing human-robot collaboration in USAR operations, and provided guidance for the next generation of autonomous robots