We explore an on-line problem where a group of robots has to reach a target whose position is known in an unknown planar environment whose geometry is acquired by the robots during task execution. The critical parameter in such a problem is the physical motion time, which, under the assumption of uniform velocity of all the robots, corresponds to length or cost of the path traveled by the robot which reached the target. The Competitiveness of an on-line algorithm measures its performance relative to the optimal off-line solution to the problem. While competitiveness usually means constant relative performance, this paper uses generalized competitiveness, i.e. any functional relationship between on-line performance and optimal off-line solution. Given an on-line task, its Competitive Complexity Class is a pair of lower and upper bounds on the competitive performance of all on-line algorithms for the task, such that the two bounds satisfy the same functional relationship. We prove that in general any on-line navigation algorithm must have at least a quadratic competitive performance. This paper describes a new on-line navigation algorithm, called MRBUG (short for Multi-Robot BUG), which requires constant memory and has a quadratic competitive performance. Thus, the above mentioned problem is classified into a quadratic competitive class. Moreover, since MRBUG achieves the quadratic lower bound, it has optimal competitiveness. The algorithm performance is illustrated in office-like environments.