Barzin Doroodgar

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Current applications of mobile robots in urban search and rescue (USAR) environments require a human operator in the loop to help guide the robot remotely. Although human operation can be effective, the unknown cluttered nature of the environments make robot navigation and victim identification highly challenging. Operators can become stressed and fatigued(More)
Teleoperated rescue robots designed to explore disaster scenes and find victims face serious limitations due to the cluttered nature of the environments as well as the rescue operators becoming stressed and disoriented in these scenes. An alternative to using teleoperated control is to develop fully autonomous controllers for rescue robots. However, these(More)
Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing a human operator to cooperate and share such tasks with a rescue robot as navigation, exploration, and victim identification. In this paper, we present a unique hierarchical(More)
Robotic urban search and rescue (USAR) is a challenging yet promising research area which has significant application potentials. This paper presents the development of a hierarchical reinforcement learning (HRL) based semi-autonomous controller for a rescue robot team working in cluttered and unstructured USAR environments. The HRL technique is introduced(More)
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