The interaction between humans and robots is undergoing an evolution. Progress in this evolution means that humans are close to robustly deploying multiple robots. Urban search and rescue (USAR) can benefit greatly from such capability. The review shows that with state of the art artificial intelligence, robots can work autonomously but still require human supervision. It also shows that multiple robot deployment (MRD) is more economical, shortens mission durations, adds reliability as well as addresses missions impossible with one robot and payload constraints. By combining robot autonomy and human supervision, the benefits of MRD can be applied to USAR while at the same time minimizing human exposure to danger. This is achieved with a single-human multiple-robot system (SHMRS). However, designers of the SHMRS must consider key attributes such as the size, composition and organizational structure of the robot collective. Variations in these attributes also induce fluctuations in issues within SHMRS deployment such as robot communication and computational load as well as human cognitive workload and situation awareness (SA). Research is essential to determine how the attributes can be manipulated to mitigate these issues while meeting the requirements of the USAR mission.
Using a single human to supervise multiple robots helps to address manpower constraints while deriving the benefits of multiple-robot deployment such as efficiency and improved system reliability. However, it can also induce high supervisor workload and diminish situation awareness. This article explains workload and situation awareness. It reviews various studies related to human-robot systems to illustrate the effects and causes of workload and diminished situation awareness in such systems. The article reviews and discusses the application of automation to address workload and situation-awareness concerns. It also presents the issues that the use of automation can cause, highlighting that automation must be applied with care. The article advocates the consideration of sliding autonomy for four aspects of task execution: information acquisition, information analysis, decision selection and action implementation. It additionally encourages the appreciation for recognized methods of applying and triggering automation. The hope is for robots to be equipped with adjustable autonomy across multiple aspects of task performance to create robotic systems with highly flexible autonomy configurations. While robots from such systems may have the flexibility to deal with numerous situation requirements, the research challenge is understanding if and how such flexibility will affect human workload.
Mobile robots can serve to augment the capabilities of rescuers for urban search and rescue (USAR) situations but must be supervised by humans. As such, the envisioned application for this research is to deploy a human supervised mobile robot to perform the search aspect of a USAR mission. This paper presents a framework developed to imbue a mobile robot with the intelligence to perform the searcb task under the guidance of a human supervisor. The framework enables the robot to perform area coverage as well as to search of victims visually using computer vision.Preliminary experiments to test the framework are also described. We find that the framework is suitable for implementation on a mobile robot for its envisioned purpose.
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