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In this project have been tackling an urban search-and-rescue (USAR) case study: we focus on planning under uncertainty methodology and algorithms for heterogeneous teams of robots involved in USAR tasks. The task of the robot team is to search for victims in an urban area, for instance after an earthquake disaster. Initially, the location of potential victims is unknown, which leads to uncertainty in a robot's observations: the robot is not able to tell (with high probability) whether a victim is present at a particular location, until it is close to the location. The robots are equipped with a high-level topological map of the area, a graph representation in which nodes can denote locations and the edges are possible routes a wheeled robot can take. As the robots explore the area, sharing information regarding located victims or blocked routes will increase team performance. However, the robots will have to cope with a potentially unreliable ad hoc wireless network. The uncertainty regarding the current and future ability to communicate has to be taken into account in a robot's plan. We implement our algorithms in a realistic simulator, USARSim.

Blimp and land robot in USARSim Blimp on leash