Service robots have a great potential of improving human quality of life by aiding in everyday tasks. However, robots that share an environment and interact with humans still face some challenges that limits their acceptance. One of these challenges is how to move and behave among groups of people, which is a task performed seamlessly by humans and some animals. This is an interesting issue for the robotics community, as it is important to predict and adapt to an environment that is constantly changing, while at the same time respect social conventions. Path planning in dynamic environments has been addressed mostly by predicting future position of humans and avoiding them. However, with the increase of the number of persons in such environments, techniques that are based only on the prediction of the movement of humans can fail, as they usually ignore the human's reaction to the presence of the robot. Instead of trying to model the complex human motion behavior, this work proposes to rely on humans to guide the robot through difficult situations, where classical approaches would fail to find a solution. This will be accomplished by a probabilistic approach for electing a human leader, according to the robot's desired destination. In this way, the robot can take advantage of the humans' paths and behavior, effortlessly avoiding dynamic and static features as the human leader does, relieving the robot from the burden of having to generate its own path in difficult situations.