Robotics and Intelligence Systems >> Urban Search and Rescue >> Technical Approach

Urban Search and Rescue

Technical Approach

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As it explores the environment either autonomously or with varying degrees of user input, the INL robot builds a real-time map of the environment. The robot is able to detect human victims based on their heat signature. By pinpointing victims on a map, the INL system permits search and rescue personnel to quickly locate these victims. A salient feature of the INL approach to robotic search and rescue is the use of multiple, distinct modes of autonomy which allow the user to shift the level of robot initiative as needed throughout the task. Experiments with experienced and novice robot operators have shown that these levels of autonomy enable users to successfully utilize the system regardless of their experience or their level of trust. As capabilities and limitations change for both the human and robot due to workload, communication dropouts, and other factors, the system can shift seamlessly from one mode into another.

Picture of INL’s search and rescue robot. INL’s search and rescue robot.

This unique approach to robotic search and rescue allows the human operator to treat the robot as a teammate instead of a passive tool. Usability studies including Federal Emergency Management Agency (FEMA) personnel, military personnel, police officers, remote operators from a nuclear cleanup site and over one thousand novice users indicate that the robot’s ability to navigate autonomously through difficult terrain exceeds the ability of human operators to teleoperate. The ability of the robot to protect itself, make decisions, and accomplish task objectives without human assistance, challenges existing assumptions regarding authority and trust.

In order to support these levels of autonomy, the INL has developed a cognitive, collaborative workspace – a real-time, shared map of the environment that allows the operator to add meaningful symbols such as victims, hazards, and other environmental entities into the map. Likewise, the robot can autonomously identify certain elements of the environment and may automatically add these to the workspace. The human may then choose to verify or annotate these entities. The end result is that the robot and human can communicate naturally and efficiently based on these map entities. For instance the robot may inform the human that a victim has been found near door 3 or the human may tell the robot to go through door 6 and search the area designated as room 3 for signs of human heat.


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