This work evaluates and improves agent-based approaches for exploration of unknown terrain and finding (shortest) paths to a goal with an unknown location. This scenario is typically found in emergency incidents and for technically assisted search and rescue cases, e.g. when a building or a block of a city has been damaged by an earthquake and as a result, former maps are not valid anymore. In our approach we simulate the exploration of the unknown environment by agents that have different capabilities typically found in the technical assistance systems used in search and rescue operations, such as cameras, sensors, and limited communication abilities. The outcome of our simulation experiments indicates, which composition of a population of agents is best suited for efficient exploration of the unknown terrain, in terms of speed and path length. The results give hints which agent populations should be tried in a real world setting, in a search and rescue test scenario with real robots and sensors.