In the multi-robot area coverage problem, a group of mobile robots have to cover an initially unknown environment using a sensor or coverage tool attached to each robot. Multi-robot area coverage is encountered in many applications of multirobot systems including unmanned search and rescue, aerial reconnaissance, robotic demining, automatic lawn mowing, and inspection of engineering structures. We envisage that multi-robot coverage can be performed efficiently if robots are coordinated to form small teams while covering the environment. In this paper, we use a technique from coalitional game theory called a weighted voting game that allows each robot to dynamically identify other team members and form teams so that the efficiency of the area coverage operation is improved. We propose and evaluate a novel technique of computing the weights of a weighted voting game based on each robot's coverage capability and finding the best minimal winning coalition(BMWC). We theoretically prove the feasibility of our model, and give algorithms to find the BMWC as well. We have also evaluated the performance of our algorithms within a robot simulation platform using up to 40 robots.